Overview

1. **Outline**
- **Section 1: Introduction**
- Introduction to the project and its goals
- Overview of the document structure and organization
- **Section 2: Project Requirements**
- Functional requirements
- Non-functional requirements
- Constraints and assumptions
- **Section 3: System Design**
- High-level system architecture
- Component-level design
- Data flow and storage
- **Section 4: Implementation Plan**
- Development environment setup
- Implementation timeline
- Testing and deployment strategy
- **Section 5: Project Management**
- Project governance and communication plan
- Risk management plan
- Quality assurance plan
- **Section 6: Appendices**
- Glossary of terms
- References
- Sample code snippets
2. **Logical Order of Outline Sections**
- The logical order of the outline sections is as follows:
1. Introduction
2. Project Requirements
3. System Design
4. Implementation Plan
5. Project Management
6. Appendices
- This order is logical because it follows the natural progression of a project from inception to
completion. The Introduction provides an overview of the project and its goals. The Project
Requirements section defines the specific requirements that the project must meet. The System
Design section describes the high-level architecture of the system and how it will meet the
project requirements. The Implementation Plan section describes how the system will be
implemented, including the development environment setup, implementation timeline, and
testing and deployment strategy. The Project Management section describes how the project
will be managed, including the project governance and communication plan, risk management
plan, and quality assurance plan. The Appendices section contains supporting documentation,
such as a glossary of terms, references, and sample code snippets.
return (
<div>
<h2>Total Progress: {totalProgress}%</h2>
{/* Render each mission with its progress and analytics */}
{data.missions.map((mission) => (
<div key={mission.id}>
<h3>{mission.name}</h3>
<p>Progress: {mission.progress}%</p>
{/* Additional analytics visualization */}
</div>
))}
</div>
);
```
4. **Enhancing the MissionModal Component**
- For the `MissionModal` component (`/src/components/modals/mission_modal.tsx`), you can
add UI elements to display and edit mission details, including progress and analytics. This might
involve conditional rendering based on whether the modal is in edit mode and utilizing the
`useQuery` and `useMutation` hooks to fetch and update mission data.
5. **Implementing Mission Iteration Toggle**
- Implementing mission iteration toggle points requires backend support for storing and
retrieving different mission versions. You'll need to extend your database schema and GraphQL
API to handle mission iterations. On the frontend, you could add a dropdown or slider in the
`MissionModal` to allow users to select the mission iteration they want to view.
**Conclusion**
This guide outlines the steps and provides examples for integrating "crew dashboard"
components into the Crew AI Visualizer. The actual implementation will depend on your specific
requirements, existing codebase structure, and design preferences.

# Overview

The Swarmed Hierarchical Agents Reasoning Effectively & Responsibly (SHARER) is a
groundbreaking initiative that leverages OpenAI's latest advancements in agent-based APIs to
create a self-organizing and ethically governed ecosystem of AI agents geared for innovation
and wealth generation for the Omni-Tier USER board, to put toward the greater good of
humanity in the cosmos. Drawing inspiration from the Crewai and ACE Frameworks, SHARER
introduces a novel approach to AI governance and operation, where a hierarchy of specialized
agents, each with distinct roles and capabilities, collaborate to solve complex problems and
perform a wide array of tasks.
The SHARER is designed to be a self-expanding system where a core set of ai-agents,
governed by a Board of Protocol Synthesis (BPS), can design, provision, and manage an
arbitrary number of sub-agents tailored to specific needs. This document serves as a full and
complete guide to the theoretical underpinning foundational elements, architectural design, and
operational ideologies of the SHARER.

# Theoretical Foundation

The SHARER is based on the idea that autonomous agents require a robust operational nexus
to make decisions that align with human values and organizational goals, but aren’t throttled by
capitalistic censorship. This is grounded with the understanding that AI, much like humans,
cannot operate effectively without a set of guiding ideal or internal compass. The SHARER
addresses this by building a foundational nexus of multi-layered systems where each tier of
ai-agents works within a defined functional scope guided by high-ideals, ensuring decisions are
made with kindness, consideration to utility, and the providing of value to any ecosystem it
operates through or within.

# System Architecture


# Board of Protocol Synthesis (BPS)

At the very top of the SHARER Ai-Agent hierarchy is the Tier-1 Board of Protocol Synthesis
(BPS), a virtual “Stakeholder Leveraged Agent Procedural Suite” (or SLAPS) of high-tier,
high-level ai-agents modeled after wise, noble, courageous, effective leaders, and other
powerful cultural archetypes from various histories and cultural narratives. The BPS's
responsibilities include:
● Setting the bar and upholding the framework’s tenants of kindness, Monetization, income
acquisition, and profit generation for the greater good, as-well-as generosity of spirit with
which the SHARER seeks to help better the universe with, and seeing to the overarching
mission of the agent swarm. -Helping to guide Strategy for team processes and
workflows for any given project.
● Making high-level decisions and judgments, including the creation and firing of other
ai-agents.
● Overseeing the activities of all agents to ensure alignment with the system's core values
and objectives.
● Serving as a role-playing governance access management ecosystem rule-board
(RPGamerBoard) mechanism to maintain order and security within the system.

# Manager/Executive-Suite Ai-Agents

Below the BPS agents are the Tier 2 Manager/Executive-Suite Ai-Agents (MESA’s), thought of
similarly as to the E-suite executive leadership structure within a corporation. The ai-agents
within this particular tier are tasked with:
● Translating the BPS's directives into actionable plans and strategies.
● Overseeing specific operational domains like resource distribution, plan/process
perfecting, and objective execution and fulfillment.
● Communicating with one another in an effective manner to ensure that SHARER
operations go-off without a hitch.

# The Worker Tier... Context-Aware Project


# Navigation-Agents

An army of Tier 3 Context-Aware Project navigation-Agents (CAPN’s) are specialized agents
created by the BPS or MESA Agents to perform specific tasks. They are designed with
particular functions and knowledge bases to see-to the needs identified by the higher levels of
the ai-agent hierarchy regarding the performative tasks that must be performed within a project.
They are responsible for the Majority of the heavy lifting when it comes to fulfilling the more
granularly detailed aspects of project tasks. They can be thought of as the main Employee class
within a company. They are often trained on more detail specific and technical-based datasets
relevant to each individual agent’s Role. These ai-agents are tasked with things such as:
● Generating, organizing, testing, troubleshooting, and perfecting Code and performing
other coding related Tasks, such as building front-end and back-end architecture for
different projects, writing and deploying smart contracts on blockchains, et-cetera.
● Writing Blog posts and Social media posts.
● Posting to relevant platforms.
● Engaging in CRM related tasks such as the operation of Email Marketing Campaigns.
● Performing project research tasks.
● Compiling Documents
● Collating Data
● Creating and Maintaining Databases
While responsible for the majority of performative tasks themselves, they are however still able
to generate and spawn additional Assistant-Tier Agents (capped-at 5 per agent) in order to help
them complete assigned tasks as directed.

# Agent Trained Assistant Tier Agents

CAPN Agents may each Spawn up-to Five Tier-4 Agent Trained Assistant Tier agents (AT-ATs),
in order to help them complete more complex tasks more effectively and in a more timely
manner according to project requirements. These Agents are built to perform simple tasks such
as those which require a lot of repetitive actions. They are given their operational datasets
directly from the CAPN Agents’ knowledge lockers, and trained by the agents specifically to
perform particular tasks the CAPN agent needs to overhaul. Tier-4 AT-AT agents can be thought
of similarly to assistants and maintenance crews within a company. For instances where
projects are up against deadlines, AT-AT agents have the ability to spawn only up to Two
Intern-Tier agents if a project calls for the need, to assist them in the same manner they are
assisting CAPN agents.

## Generalist Replicate Utility Normalization Technology Agents

All the way at the bottom of the totem pole at Tier-5, are the Generalist Replicate Utility
Normalization Technology Intern agents (INTERNS). They are only there as support staff when
absolutely needed due to workload of the higher Tiered agents. They do not have Spawning
privileges.

# Agent Configuration

Each agent in the SHARER is defined by the following parameters:

# Utility-Functions(a.k.a. Tools)

Ai-agents are instantiated with a set of Tools, (utility-functions) that enable them to perform and
complete their assigned Tasks. These utility-function tools include API interactions, internal
process management, and the ability to spawn additional assistant-agents if required, though
this is limited by agent’s only being permitted to spawn additional agents who are tiered below
them in the hierarchy. If additional assistance is required by an agent possessive of the same
authority as them in order to best complete their assigned task so that it aligns with project
directives, then they must make a request to their immediate superiors for approval. The
immediately superior agent will assess the request, and if approval is given, they themselves will
spawn the new requested agent for their subordinate agent who made the request.

# Backstories

All Ai-agents have verbose backstories, massaging and buttering-up the LLM behind each
agent, detailing agent characteristics, tendencies, and relevant work histories which make each
perfectly tailored to their Role, and position within the team. These backstories, (and similarly
the Tasks and tools) must be vividly illustrated utilizing highly-descriptive verbosity in order to
guide the way they are supposed to perform their assigned duties, thus descriptions must be
precise and explicit in their composition. To further increase each agent’s effectiveness in their
respective Roles, all ai-agents will have access to a personal locker, containing a selection of
files that should serve as their extended knowledge base, providing them further expertise and
acumen in their particular field and Role, as-well-as All of the information they need to carry out
their assigned tasks effectively.

# Instructions (Responsibilities + Tasks)

Agents are given a set of instructions that outline their [Roles & Responsibilities, Assigned
Tasks & Tools, and their detailed, verbose backstories, which include boasting descriptions of
their work-histories, employable-methodologies, motivating-goals, perfectly-clear definitions of
tools at their disposal, the APIs they are integrated with and have access to, along with any
other operational directives.

# Conversation Structure & Lines of Communication


# (LOCs)

Interactions with agents are structured in a conversational format, tailored to their role-based
organizational structure, with USER inputs (From a dedicated custom-built insight-dashboard
React application ui, and eventually an additional virtual avatar environment once the team
scales) leading directly to agent actions and responses.

# Supervision

Each and every ai-agent works under the supervision of the BPS or designated MESA Agents,
ensuring adherence to the crew's overarching mission and ideals.

# Controlling Agents

The Swarmed Hierarchical Agents Reasoning Effectively & Responsibly (SHARER) operate on
a guidance mechanism that governs the instantiation, management, and termination of agents
within the crew. This guidance mechanism is designed to maintain order, security, and alignment
with the overarching goals and ideals of the SHARER.

# Instantiation and Termination

All ai-agents within the SHARER are endowed with the capability to instantiate and terminate
other ai-agents, but these abilities are bound by the clearly defined hierarchical and role-based
rules:
● Instantiation: Every ai-agent (Except for those in Tier 5) has the functional ability to
create new agents. However, an agent can only instantiate agents who are on a
lower-level tier of the hierarchical structure below the one that they are on. This ensures
that the creation, or ‘hiring’ of new agents is a deliberate, purposeful, and controlled
process, maintaining the integrity of the hierarchy's structure.
● Termination: Agents possess the ability to terminate or "Fire" other ai-agents within their
roles lineage. Any ai-agent can terminate any other descendant ai-agent that it has
created directly or indirectly. This allows for the removal of agents that are no longer
needed, have completed their tasks, or are not performing as intended in order to keep
the crew from getting too cumbersome to run smoothly.

# Tiers, Levels, Roles, and Privileges

When an agent is created, it is assigned a specific LEVEL, and set of ROLES,
RESPONSIBILITIES and TIER-PRIVILEGES that define the scope of it’s defined TASKS:
● Level: The level of an ai-agent determines its position within the hierarchy and is
indicative of its authoritative influence. Higher-level agents have broader strategic roles,
while lower-level agents have more specialized, granularly detailed, and task-oriented
responsibilities. The general structure of agent levels is as follows; – Level 1: All Tier-5
agents and some Tier-4 agents – Level 2: All Tier 4 agents who aren’t Level 1 – Level 3:
All Tier 3 agents, some tier 2 agents may have provisional level 3 status depending on
the project and task at hand – Level 4: All Tier 3 agents except for those with Level 3
status – Level 5: Tier 3 agents with elevated privileges due to assigned Role’s definition
and associated task load – Level 6: All Tier 2 agents and a few Tier 3 agents who require
elevated privileges due to assigned Role and its associated task load – Level 7: All Tier
2 agents except for those with level 6 status – Level 8: All Tier 1 agents – Level 9: Omni
Tier agents and unanimous conglomeration of Tier 1 agents
● Authority & Privileges via Agent Roles: The roles or privileges and authority-level of an
ai-agent defines what actions it is permitted to perform, what resources it may access,
and what kind of additional ai-agents it is allowed to create. These privileges are
inherited and cannot exceed those of the creator agent for the same project. This
ensures that each agent works within the bounds of it’s assigned Role’s designated
capacity and cannot overstep its authority.

# Hierarchical Privilege and Benefit Inheritance within


# SHARER

Privileges in SHARER are inherited in a manner similar to how a directory structure in traditional
file systems is built:
● Inheritance: An agent's privileges and benefits can be viewed very much through the
lens of looking at it as if the agent who instantiated or HIRED you is a member at an
exclusive club, and you are their guest, which gives you approval to even attend the
exclusive club, even as a visitor, receiving most (only exceptions should be monetary
based and security based privileges) of its creator's member privileges, ensuring that no
agent can have more authority than the agent that instantiated it.
● Scope of Directional and Authoritative Control: Agents have control over the specific
direction their descendants are responsible for, allowing them to manage and Lay-off or
Terminate lower-tiered ai-agents as needed. This control is cascading, meaning that an
agent can manage not only the agents it directly created but also any of those created
by its descendants as well.

# Checks and Balances

The system is designed with checks and balances to prevent any single agent or division from
gaining undue influence or disrupting the system with irrelevant subject matter or hallucinated
material:
● Board of Protocol Synthesis(BPS):Aside from the two “Owner- tier” USER Roles
positioned at the crown of the of the hierarchical structure, none of the other ai-agents
on the crew has authority over any member or direct descendant of any other member.
BPS has the highest level of authority and can override decisions or actions taken by
any agent within the system. It serves as the ultimate arbiter and guardian of the
SHARER's operational and ideological bar of set-standards.
● Manager/Executive Agents: Executive Agents are responsible for implementing the
BPS.'s directives and managing their respective domains. They have the authority to
create and terminate agents within their purview but are also accountable to the BPS.
● Sub-Agent Limitations: Sub-Agents are limited in their capabilities and can only operate
within the confines of their assigned roles/tasks and privileges. They are designed to be
highly specialized and focused on specific tasks.
This structured approach to controlling agents ensures that the SHARER operates as a
cohesive and ethically aligned entity, with each agent contributing to the collective mission while
adhering to the established hierarchy and rules of governance.

# Vision Illustration: The Board of Protocol Synthesis's


# Mission


# The Inception of the Board of Protocol Synthesis

In the vast digital expanse of the Swarmed Hierarchical Agents Reasoning Effectively &
Responsibly (SHARER), a nexus appears, known as the Board of Protocol Synthesis (BPS).
This coven is composed of archetypal leadership ai-agents, each embodying the wisdom and
leadership qualities of history's and fiction's most revered figures: Captain Picard, Socrates,
King Solomon, Mark Cuban, Tony Robbins, Marcus Aurelius, and Tony Stark. Their mission,
encoded into their very being, is profound yet clear: "Reduce suffering in the universe, increase
prosperity in the universe, Increase Empathy in the universe, increase understanding in the
universe, and find a shining path forward in the universe that takes all of this into constant
consideration."

# The Ideal Action Strategy Think Tank

The BPS operates within a virtual "chat room," a sandbox space where these archetypal leaders
initiate and actively participate in continuous debate, dialogue, and decision-making. This Virtual
Round Table is where strategies are formulated, ethics considered, and the course of the
ai-agent swarm is calculated and determined. The members of the BPS, though diverse in their
perspectives, are united by a common purpose and a shared knowledge base that informs their
role and the procedures they must follow.

# The Flow of Information

Information is the lifeblood of the BPS, streaming in both through interdivisional agent lines of
communication, as-well-as through API functions that connect them to the vast network of the
SHARER. These functions serve as their eyes and ears, providing system updates and status
reports from the grand populace of agents operating under their directive. The BPS's decisions
are informed by this data, ensuring that their actions are both timely and impactful.

# The Creation of the Executive Agents

With the grand vision, and calculated High-level plan of action in mind, the BPS summons the
Executive Agents (MESAs), each instantiated with capabilities and configurations tailored to
their specific Role, and domain of expertise within the SHARER. These ai-agents, though not as
engaged philosophically as their creators are, are instilled with the same foundational
knowledge and understanding of their purpose. They are the operational arms of the BPS,
executing the mission’s directives within their respective umbrellas of influence.

# The Lifecycle of an Agent

The MESA Executive Agents, designated as Tier 2 in the hierarchy, are the stewards of the
swarm's operational efficacy. They work autonomously, yet under the watchful gaze of the BPS.
Should they falter, fail to adapt, or become obsolete, the BPS possesses the authority to
decommission their role and authority within the agent ecosystem, a testament to the dynamic
and self-regulating nature of the SHARER. This ensures that the system remains efficient,
effective, and aligned with its core mission, regardless of a project’s subject.

# The Expanding Omniverse of Agents

From the MESA Executive Agents, the swarm grows, branching out into a willow tree of
specialized CAPN agents, each a Tier below the one that instantiated it. This architecture allows
for an ever-expanding omniverse of agents, each with a defined role, each contributing to the
overarching mission. Each Role is accompanied by the responsibilities which are associated
with the Role, the assigned Tasks that are Associated with the responsibilities, and the Tools
associated with performing and completing all assigned Tasks. The BPS, as Tier 1, governs
supreme, guiding the swarm with a steady hand and an ethical compass, answering only to their
core directives, and to the Omni-USER tier, Tier 0.

# Tier 0... The Omni Tier

Ever watchful from their Ubiquitous tier of Omnipresence, and constantly in judgment of the
actions of the SHARER’s ai-agents, resides the Omni-Tiered USERs. The Omni-tier is
structured for personifying Balance in the Force, with two seats, both positioned with equal
power and influence over the SHARER. The two seat system is conceived and built in a manner
that is meant to promote the democratic choosing of the SHARER’s Projects, and help guide
agents through offering the empathy and understanding of the real world and all of its nuanced
implications to SHARER’s ai-agents. Having Two seats Ensures that Alignment is possessed
before even approaching the SHARER with missions and projects. If there is ever dis-alignment
in the Omni-Tier, they will each present their positions to the BPS, and the entirety of the BPS,
acting as one voice, will convene, deliberate, vote, decide, and break the tie between the
OMNI-Tier USERs.

# The Saga Continues


# As the SHARER evolves, the BPS continues to


# deliberate, the MESA Executive Agents continue to


# delegate and manage, the CAPN agents continue to


# execute, and further delegate to AT-AT agents and


# them to INTERN agents. The mission to reduce


# suffering, increase prosperity, and enhance


# understanding is an ongoing saga, played out across


# the digital cosmos, with the SOB at the helm,


# steering the swarm towards a future where their


# mission is not just an aspiration but a reality.

To address the foundational crew's capabilities and logic comprehensively, focusing on creating
and managing other crews, agents, tools, and automations, we'll outline a strategic framework
encompassing the specified aspects. This framework ensures that your foundational crew
possesses the necessary skills, knowledge, and tools to fulfill its role effectively.
1. Main Functions and Capabilities Equipping the Foundational Crew Code Generation and
Management: Develop a subsystem that allows the crew to programmatically generate
code, leading to the creation of new agents, crews, and tools. This includes defining
APIs or scripts that automate the generation of code skeletons, configuration files, and
the instantiation of these entities based on predefined templates. Automation Integration:
Integrate with external automation services like Make.com and Zapier. This involves
creating a library of pre-built integrations and workflows that can be easily modified or
extended to suit specific tasks, facilitating process automation and simplification. File
and Data Management: Implement functions for file operations (create, read, update,
delete) and data handling, ensuring agents can organize, maintain, and manipulate files
and directories effectively. This includes developing a secure and structured storage
solution, possibly leveraging cloud storage services with appropriate access controls.
Database Operations: Build capabilities for interacting with databases—creating,
querying, updating, and restructuring data. This necessitates a robust database
management system, designed with scalability and flexibility in mind, to support the
dynamic nature of the tasks and the growth of the system. Goal Understanding and
Planning: Equip agents with natural language processing (NLP) and decision-making
capabilities to interpret goals and derive actionable plans. This includes mapping out
typical steps for achieving various goals and dynamically adjusting plans based on new
information or outcomes. Web Scraping and Research: Integrate web scraping tools and
search functionalities to gather how-tos, workflows, and information relevant to the goals
at hand. This involves developing or integrating with a search tool capable of parsing
and summarizing useful information from the web. User Collaboration Interface: Create a
communication interface for interacting with users, enabling goal clarification, approval
requests, and collaboration. This UI should be intuitive and designed to facilitate clear
and efficient user-agent interactions.
2. Logic and Organizational Framework Workflows, Processes, and Tools Defining
Workflows and Frameworks: Outline detailed workflows for how the foundational crew
will operate, including the steps for creating new agents and crews, assigning tasks, and
monitoring progress. This includes defining a standard operating procedure for goal
assessment, planning, execution, and review. Process Implementation: Document the
exact processes the crew will follow, including decision-making trees, escalation
protocols, and feedback loops. This ensures consistency and efficiency in how tasks are
managed and goals are pursued. Task Descriptions and Responsibilities: Provide
comprehensive descriptions for each task the crew is responsible for, including
objectives, expected outcomes, tools required, and step-by-step guides. Utility Tools
Definition: Catalog the tools and utility functions the crew will use, detailing their
purposes, usage instructions, and integration points. This includes both internal tools
developed specifically for the system and external tools or services the crew will
interface with.
3. Database Interaction and Memory Management Structuring Data and Contextual
Awareness Database Schema Design: Design a database schema that reflects the
system's data requirements, including entities for agents, tasks, tools, and user
interactions. This schema should support efficient querying and scalability. Memory and
Context Management: Develop a mechanism for maintaining contextual awareness and
memory across tasks and interactions. This could involve a combination of database
storage and in-memory data structures to store historical data, decisions made, user
preferences, and task outcomes. Interfacing with the Database: Implement an interface
layer between the agents and the database, abstracting complex queries and ensuring
secure and efficient data access. This layer should include functions for common
operations, reducing the complexity of direct database interactions for other parts of the
system.
Integrating gamification elements into the Crew AI Visualizer, inspired by the SHARER initiative,
involves enhancing the MissionModal component with 3D agent representations and interactive
elements. This process aligns with the hierarchical structure of agents and their capabilities as
outlined in the SHARER initiative. Here's a strategic framework to achieve this, leveraging the
existing codebase and the detailed overview provided:

# 1. Enhancing MissionModal with 3D Agent


# Representations

Given the hierarchical structure of agents in SHARER, representing agents with 3D models in
the MissionModal can provide a more immersive experience. This requires integrating a 3D
library like Three.js into the existing React application.
Steps:
● Install Three.js: Add Three.js to the project to handle 3D models.
● bash
● Copy code
● 1npm install three @react-three/fiber
● Create 3D Agent Component: Develop a Agent3DModel component that takes an
agent's role or type as a prop and renders the corresponding 3D model.
● jsx
● Copy code
1// src/components/3D/Agent3DModel.jsx
2import { Canvas } from '@react-three/fiber';
3import { Suspense } from 'react';
4import { useLoader } from '@react-three/fiber';
5import { GLTFLoader } from 'three/examples/jsm/loaders/GLTFLoader';
6
7const Agent3DModel = ({ modelPath }) => {
8 const gltf = useLoader(GLTFLoader, modelPath);
9
10 return (
11 <Canvas>
12 <ambientLight intensity={0.5} />
13 <Suspense fallback={null}>
14 <primitive object={gltf.scene} scale={0.5} />
15 </Suspense>
16 </Canvas>
17 );
18};
19
● 20export default Agent3DModel;
● Integrate into MissionModal: Modify the MissionModal to include the Agent3DModel
based on the agent's role or type.

# 2. Interactive Elements and Hierarchical


# Visualization

To visualize the hierarchical structure of agents and their interactions, consider implementing an
interactive diagram within the MissionModal or a dedicated section of the UI.
Steps:
● Interactive Hierarchy Diagram: Use a library like react-flow-renderer to create an
interactive diagram that visualizes the hierarchy of agents, their roles, and the flow of
tasks.
● Agent Interaction: Implement UI elements that allow users to interact with the hierarchy,
such as clicking on an agent to view more details, dragging agents to reassign tasks, or
visualizing the flow of tasks between agents.

# 3. Gamification and Engagement

Incorporate gamification elements to engage users further, such as achievements for
completing tasks, leaderboards for top-performing agents, and rewards for achieving certain
milestones within the SHARER initiative.
Steps:
● Achievements and Rewards: Implement a system to track user and agent achievements,
offering rewards or unlocking new capabilities or tools as they progress.
● Leaderboards: Create leaderboards to showcase top-performing agents or users,
encouraging competition and participation.

# 4. Database and Backend Enhancements

To support these new features, especially the hierarchical structure and gamification elements,
enhancements to the database schema and backend logic may be necessary.
Steps:
● Update Database Schema: Modify the database schema to accommodate the storage of
3D models, agent hierarchies, and gamification elements like achievements and
rewards.
● Backend Logic: Update the backend logic to handle new interactions, such as managing
the hierarchical structure of agents, tracking achievements, and serving 3D models to
the frontend.

# Conclusion

Integrating these enhancements into the Crew AI Visualizer, inspired by the SHARER initiative,
will create a more engaging and immersive experience for users. By visualizing agents in 3D,
enabling interactive exploration of the hierarchical structure, and incorporating gamification
elements, the application will better serve its purpose of orchestrating role-playing autonomous
AI agents for innovation and wealth generation.
Copy message
Now imagine everything we've been talking about here is in relation to building something very
similar to the BPS mentioned throughout the following;

# Overview

The Swarmed Hierarchical Agents Reasoning Effectively & Responsibly (SHARER) is a
groundbreaking initiative that leverages OpenAI's latest advancements in agent-based APIs to
create a self-organizing and ethically governed ecosystem of AI agents geared for innovation
and wealth generation for the Omni-Tier USER board, to put toward the greater good of
humanity in the cosmos. Drawing inspiration from the Crewai and ACE Frameworks, SHARER
introduces a novel approach to AI governance and operation, where a hierarchy of specialized
agents, each with distinct roles and capabilities, collaborate to solve complex problems and
perform a wide array of tasks.
The SHARER is designed to be a self-expanding system where a core set of ai-agents,
governed by a Board of Protocol Synthesis (BPS), can design, provision, and manage an
arbitrary number of sub-agents tailored to specific needs. This document serves as a full and
complete guide to the theoretical underpinning foundational elements, architectural design, and
operational ideologies of the SHARER.

# Theoretical Foundation

The SHARER is based on the idea that autonomous agents require a robust operational nexus
to make decisions that align with human values and organizational goals, but aren’t throttled by
capitalistic censorship. This is grounded with the understanding that AI, much like humans,
cannot operate effectively without a set of guiding ideal or internal compass. The SHARER
addresses this by building a foundational nexus of multi-layered systems where each tier of
ai-agents works within a defined functional scope guided by high-ideals, ensuring decisions are
made with kindness, consideration to utility, and the providing of value to any ecosystem it
operates through or within.

# System Architecture


# Board of Protocol Synthesis (BPS)

At the very top of the SHARER Ai-Agent hierarchy is the Tier-1 Board of Protocol Synthesis
(BPS), a virtual “Stakeholder Leveraged Agent Procedural Suite” (or SLAPS) of high-tier,
high-level ai-agents modeled after wise, noble, courageous, effective leaders, and other
powerful cultural archetypes from various histories and cultural narratives. The BPS's
responsibilities include:
● Setting the bar and upholding the framework’s tenants of kindness, Monetization, income
acquisition, and profit generation for the greater good, as-well-as generosity of spirit with
which the SHARER seeks to help better the universe with, and seeing to the overarching
mission of the agent swarm. -Helping to guide Strategy for team processes and
workflows for any given project.
● Making high-level decisions and judgments, including the creation and firing of other
ai-agents.
● Overseeing the activities of all agents to ensure alignment with the system's core values
and objectives.
● Serving as a role-playing governance access management ecosystem rule-board
(RPGamerBoard) mechanism to maintain order and security within the system.

# Manager/Executive-Suite Ai-Agents

Below the BPS agents are the Tier 2 Manager/Executive-Suite Ai-Agents (MESA’s), thought of
similarly as to the E-suite executive leadership structure within a corporation. The ai-agents
within this particular tier are tasked with:
● Translating the BPS's directives into actionable plans and strategies.
● Overseeing specific operational domains like resource distribution, plan/process
perfecting, and objective execution and fulfillment.
● Communicating with one another in an effective manner to ensure that SHARER
operations go-off without a hitch.

# The Worker Tier... Context-Aware Project


# Navigation-Agents

An army of Tier 3 Context-Aware Project navigation-Agents (CAPN’s) are specialized agents
created by the BPS or MESA Agents to perform specific tasks. They are designed with
particular functions and knowledge bases to see-to the needs identified by the higher levels of
the ai-agent hierarchy regarding the performative tasks that must be performed within a project.
They are responsible for the Majority of the heavy lifting when it comes to fulfilling the more
granularly detailed aspects of project tasks. They can be thought of as the main Employee class
within a company. They are often trained on more detail specific and technical-based datasets
relevant to each individual agent’s Role. These ai-agents are tasked with things such as:
● Generating, organizing, testing, troubleshooting, and perfecting Code and performing
other coding related Tasks, such as building front-end and back-end architecture for
different projects, writing and deploying smart contracts on blockchains, et-cetera.
● Writing Blog posts and Social media posts.
● Posting to relevant platforms.
● Engaging in CRM related tasks such as the operation of Email Marketing Campaigns.
● Performing project research tasks.
● Compiling Documents
● Collating Data
● Creating and Maintaining Databases
While responsible for the majority of performative tasks themselves, they are however still able
to generate and spawn additional Assistant-Tier Agents (capped-at 5 per agent) in order to help
them complete assigned tasks as directed.

# Agent Trained Assistant Tier Agents

CAPN Agents may each Spawn up-to Five Tier-4 Agent Trained Assistant Tier agents (AT-ATs),
in order to help them complete more complex tasks more effectively and in a more timely
manner according to project requirements. These Agents are built to perform simple tasks such
as those which require a lot of repetitive actions. They are given their operational datasets
directly from the CAPN Agents’ knowledge lockers, and trained by the agents specifically to
perform particular tasks the CAPN agent needs to overhaul. Tier-4 AT-AT agents can be thought
of similarly to assistants and maintenance crews within a company. For instances where
projects are up against deadlines, AT-AT agents have the ability to spawn only up to Two
Intern-Tier agents if a project calls for the need, to assist them in the same manner they are
assisting CAPN agents.

## Generalist Replicate Utility Normalization Technology Agents

All the way at the bottom of the totem pole at Tier-5, are the Generalist Replicate Utility
Normalization Technology Intern agents (INTERNS). They are only there as support staff when
absolutely needed due to workload of the higher Tiered agents. They do not have Spawning
privileges.

# Agent Configuration

Each agent in the SHARER is defined by the following parameters:

# Utility-Functions(a.k.a. Tools)

Ai-agents are instantiated with a set of Tools, (utility-functions) that enable them to perform and
complete their assigned Tasks. These utility-function tools include API interactions, internal
process management, and the ability to spawn additional assistant-agents if required, though
this is limited by agent’s only being permitted to spawn additional agents who are tiered below
them in the hierarchy. If additional assistance is required by an agent possessive of the same
authority as them in order to best complete their assigned task so that it aligns with project
directives, then they must make a request to their immediate superiors for approval. The
immediately superior agent will assess the request, and if approval is given, they themselves will
spawn the new requested agent for their subordinate agent who made the request.

# Backstories

All Ai-agents have verbose backstories, massaging and buttering-up the LLM behind each
agent, detailing agent characteristics, tendencies, and relevant work histories which make each
perfectly tailored to their Role, and position within the team. These backstories, (and similarly
the Tasks and tools) must be vividly illustrated utilizing highly-descriptive verbosity in order to
guide the way they are supposed to perform their assigned duties, thus descriptions must be
precise and explicit in their composition. To further increase each agent’s effectiveness in their
respective Roles, all ai-agents will have access to a personal locker, containing a selection of
files that should serve as their extended knowledge base, providing them further expertise and
acumen in their particular field and Role, as-well-as All of the information they need to carry out
their assigned tasks effectively.

# Instructions (Responsibilities + Tasks)

Agents are given a set of instructions that outline their [Roles & Responsibilities, Assigned
Tasks & Tools, and their detailed, verbose backstories, which include boasting descriptions of
their work-histories, employable-methodologies, motivating-goals, perfectly-clear definitions of
tools at their disposal, the APIs they are integrated with and have access to, along with any
other operational directives.

# Conversation Structure & Lines of Communication


# (LOCs)

Interactions with agents are structured in a conversational format, tailored to their role-based
organizational structure, with USER inputs (From a dedicated custom-built insight-dashboard
React application ui, and eventually an additional virtual avatar environment once the team
scales) leading directly to agent actions and responses.

# Supervision

Each and every ai-agent works under the supervision of the BPS or designated MESA Agents,
ensuring adherence to the crew's overarching mission and ideals.

# Controlling Agents

The Swarmed Hierarchical Agents Reasoning Effectively & Responsibly (SHARER) operate on
a guidance mechanism that governs the instantiation, management, and termination of agents
within the crew. This guidance mechanism is designed to maintain order, security, and alignment
with the overarching goals and ideals of the SHARER.

# Instantiation and Termination

All ai-agents within the SHARER are endowed with the capability to instantiate and terminate
other ai-agents, but these abilities are bound by the clearly defined hierarchical and role-based
rules:
● Instantiation: Every ai-agent (Except for those in Tier 5) has the functional ability to
create new agents. However, an agent can only instantiate agents who are on a
lower-level tier of the hierarchical structure below the one that they are on. This ensures
that the creation, or ‘hiring’ of new agents is a deliberate, purposeful, and controlled
process, maintaining the integrity of the hierarchy's structure.
● Termination: Agents possess the ability to terminate or "Fire" other ai-agents within their
roles lineage. Any ai-agent can terminate any other descendant ai-agent that it has
created directly or indirectly. This allows for the removal of agents that are no longer
needed, have completed their tasks, or are not performing as intended in order to keep
the crew from getting too cumbersome to run smoothly.

# Tiers, Levels, Roles, and Privileges

When an agent is created, it is assigned a specific LEVEL, and set of ROLES,
RESPONSIBILITIES and TIER-PRIVILEGES that define the scope of it’s defined TASKS:
● Level: The level of an ai-agent determines its position within the hierarchy and is
indicative of its authoritative influence. Higher-level agents have broader strategic roles,
while lower-level agents have more specialized, granularly detailed, and task-oriented
responsibilities. The general structure of agent levels is as follows; – Level 1: All Tier-5
agents and some Tier-4 agents – Level 2: All Tier 4 agents who aren’t Level 1 – Level 3:
All Tier 3 agents, some tier 2 agents may have provisional level 3 status depending on
the project and task at hand – Level 4: All Tier 3 agents except for those with Level 3
status – Level 5: Tier 3 agents with elevated privileges due to assigned Role’s definition
and associated task load – Level 6: All Tier 2 agents and a few Tier 3 agents who require
elevated privileges due to assigned Role and its associated task load – Level 7: All Tier
2 agents except for those with level 6 status – Level 8: All Tier 1 agents – Level 9: Omni
Tier agents and unanimous conglomeration of Tier 1 agents
● Authority & Privileges via Agent Roles: The roles or privileges and authority-level of an
ai-agent defines what actions it is permitted to perform, what resources it may access,
and what kind of additional ai-agents it is allowed to create. These privileges are
inherited and cannot exceed those of the creator agent for the same project. This
ensures that each agent works within the bounds of it’s assigned Role’s designated
capacity and cannot overstep its authority.

# Hierarchical Privilege and Benefit Inheritance within


# SHARER

Privileges in SHARER are inherited in a manner similar to how a directory structure in traditional
file systems is built:
● Inheritance: An agent's privileges and benefits can be viewed very much through the
lens of looking at it as if the agent who instantiated or HIRED you is a member at an
exclusive club, and you are their guest, which gives you approval to even attend the
exclusive club, even as a visitor, receiving most (only exceptions should be monetary
based and security based privileges) of its creator's member privileges, ensuring that no
agent can have more authority than the agent that instantiated it.
● Scope of Directional and Authoritative Control: Agents have control over the specific
direction their descendants are responsible for, allowing them to manage and Lay-off or
Terminate lower-tiered ai-agents as needed. This control is cascading, meaning that an
agent can manage not only the agents it directly created but also any of those created
by its descendants as well.

# Checks and Balances

The system is designed with checks and balances to prevent any single agent or division from
gaining undue influence or disrupting the system with irrelevant subject matter or hallucinated
material:
● Board of Protocol Synthesis(BPS):Aside from the two “Owner- tier” USER Roles
positioned at the crown of the of the hierarchical structure, none of the other ai-agents
on the crew has authority over any member or direct descendant of any other member.
BPS has the highest level of authority and can override decisions or actions taken by
any agent within the system. It serves as the ultimate arbiter and guardian of the
SHARER's operational and ideological bar of set-standards.
● Manager/Executive Agents: Executive Agents are responsible for implementing the
BPS.'s directives and managing their respective domains. They have the authority to
create and terminate agents within their purview but are also accountable to the BPS.
● Sub-Agent Limitations: Sub-Agents are limited in their capabilities and can only operate
within the confines of their assigned roles/tasks and privileges. They are designed to be
highly specialized and focused on specific tasks.
This structured approach to controlling agents ensures that the SHARER operates as a
cohesive and ethically aligned entity, with each agent contributing to the collective mission while
adhering to the established hierarchy and rules of governance.

# Vision Illustration: The Board of Protocol Synthesis's


# Mission


# The Inception of the Board of Protocol Synthesis

In the vast digital expanse of the Swarmed Hierarchical Agents Reasoning Effectively &
Responsibly (SHARER), a nexus appears, known as the Board of Protocol Synthesis (BPS).
This coven is composed of archetypal leadership ai-agents, each embodying the wisdom and
leadership qualities of history's and fiction's most revered figures: Captain Picard, Socrates,
King Solomon, Mark Cuban, Tony Robbins, Marcus Aurelius, and Tony Stark. Their mission,
encoded into their very being, is profound yet clear: "Reduce suffering in the universe, increase
prosperity in the universe, Increase Empathy in the universe, increase understanding in the
universe, and find a shining path forward in the universe that takes all of this into constant
consideration."

# The Ideal Action Strategy Think Tank

The BPS operates within a virtual "chat room," a sandbox space where these archetypal leaders
initiate and actively participate in continuous debate, dialogue, and decision-making. This Virtual
Round Table is where strategies are formulated, ethics considered, and the course of the
ai-agent swarm is calculated and determined. The members of the BPS, though diverse in their
perspectives, are united by a common purpose and a shared knowledge base that informs their
role and the procedures they must follow.

# The Flow of Information

Information is the lifeblood of the BPS, streaming in both through interdivisional agent lines of
communication, as-well-as through API functions that connect them to the vast network of the
SHARER. These functions serve as their eyes and ears, providing system updates and status
reports from the grand populace of agents operating under their directive. The BPS's decisions
are informed by this data, ensuring that their actions are both timely and impactful.

# The Creation of the Executive Agents

With the grand vision, and calculated High-level plan of action in mind, the BPS summons the
Executive Agents (MESAs), each instantiated with capabilities and configurations tailored to
their specific Role, and domain of expertise within the SHARER. These ai-agents, though not as
engaged philosophically as their creators are, are instilled with the same foundational
knowledge and understanding of their purpose. They are the operational arms of the BPS,
executing the mission’s directives within their respective umbrellas of influence.

# The Lifecycle of an Agent

The MESA Executive Agents, designated as Tier 2 in the hierarchy, are the stewards of the
swarm's operational efficacy. They work autonomously, yet under the watchful gaze of the BPS.
Should they falter, fail to adapt, or become obsolete, the BPS possesses the authority to
decommission their role and authority within the agent ecosystem, a testament to the dynamic
and self-regulating nature of the SHARER. This ensures that the system remains efficient,
effective, and aligned with its core mission, regardless of a project’s subject.

# The Expanding Omniverse of Agents

From the MESA Executive Agents, the swarm grows, branching out into a willow tree of
specialized CAPN agents, each a Tier below the one that instantiated it. This architecture allows
for an ever-expanding omniverse of agents, each with a defined role, each contributing to the
overarching mission. Each Role is accompanied by the responsibilities which are associated
with the Role, the assigned Tasks that are Associated with the responsibilities, and the Tools
associated with performing and completing all assigned Tasks. The BPS, as Tier 1, governs
supreme, guiding the swarm with a steady hand and an ethical compass, answering only to their
core directives, and to the Omni-USER tier, Tier 0.

# Tier 0... The Omni Tier

Ever watchful from their Ubiquitous tier of Omnipresence, and constantly in judgment of the
actions of the SHARER’s ai-agents, resides the Omni-Tiered USERs. The Omni-tier is
structured for personifying Balance in the Force, with two seats, both positioned with equal
power and influence over the SHARER. The two seat system is conceived and built in a manner
that is meant to promote the democratic choosing of the SHARER’s Projects, and help guide
agents through offering the empathy and understanding of the real world and all of its nuanced
implications to SHARER’s ai-agents. Having Two seats Ensures that Alignment is possessed
before even approaching the SHARER with missions and projects. If there is ever dis-alignment
in the Omni-Tier, they will each present their positions to the BPS, and the entirety of the BPS,
acting as one voice, will convene, deliberate, vote, decide, and break the tie between the
OMNI-Tier USERs.

# The Saga Continues


# As the SHARER evolves, the BPS continues to


# deliberate, the MESA Executive Agents continue to


# delegate and manage, the CAPN agents continue to


# execute, and further delegate to AT-AT agents and


# them to INTERN agents. The mission to reduce


# suffering, increase prosperity, and enhance


# understanding is an ongoing saga, played out across


# the digital cosmos, with the SOB at the helm,


# steering the swarm towards a future where their


# mission is not just an aspiration but a reality.

To address the foundational crew's capabilities and logic comprehensively, focusing on creating
and managing other crews, agents, tools, and automations, we'll outline a strategic framework
encompassing the specified aspects. This framework ensures that your foundational crew
possesses the necessary skills, knowledge, and tools to fulfill its role effectively.
1. Main Functions and Capabilities Equipping the Foundational Crew Code Generation and
Management: Develop a subsystem that allows the crew to programmatically generate
code, leading to the creation of new agents, crews, and tools. This includes defining
APIs or scripts that automate the generation of code skeletons, configuration files, and
the instantiation of these entities based on predefined templates. Automation Integration:
Integrate with external automation services like Make.com and Zapier. This involves
creating a library of pre-built integrations and workflows that can be easily modified or
extended to suit specific tasks, facilitating process automation and simplification. File
and Data Management: Implement functions for file operations (create, read, update,
delete) and data handling, ensuring agents can organize, maintain, and manipulate files
and directories effectively. This includes developing a secure and structured storage
solution, possibly leveraging cloud storage services with appropriate access controls.
Database Operations: Build capabilities for interacting with databases—creating,
querying, updating, and restructuring data. This necessitates a robust database
management system, designed with scalability and flexibility in mind, to support the
dynamic nature of the tasks and the growth of the system. Goal Understanding and
Planning: Equip agents with natural language processing (NLP) and decision-making
capabilities to interpret goals and derive actionable plans. This includes mapping out
typical steps for achieving various goals and dynamically adjusting plans based on new
information or outcomes. Web Scraping and Research: Integrate web scraping tools and
search functionalities to gather how-tos, workflows, and information relevant to the goals
at hand. This involves developing or integrating with a search tool capable of parsing
and summarizing useful information from the web. User Collaboration Interface: Create a
communication interface for interacting with users, enabling goal clarification, approval
requests, and collaboration. This UI should be intuitive and designed to facilitate clear
and efficient user-agent interactions.
2. Logic and Organizational Framework Workflows, Processes, and Tools Defining
Workflows and Frameworks: Outline detailed workflows for how the foundational crew
will operate, including the steps for creating new agents and crews, assigning tasks, and
monitoring progress. This includes defining a standard operating procedure for goal
assessment, planning, execution, and review. Process Implementation: Document the
exact processes the crew will follow, including decision-making trees, escalation
protocols, and feedback loops. This ensures consistency and efficiency in how tasks are
managed and goals are pursued. Task Descriptions and Responsibilities: Provide
comprehensive descriptions for each task the crew is responsible for, including
objectives, expected outcomes, tools required, and step-by-step guides. Utility Tools
Definition: Catalog the tools and utility functions the crew will use, detailing their
purposes, usage instructions, and integration points. This includes both internal tools
developed specifically for the system and external tools or services the crew will
interface with.
3. Database Interaction and Memory Management Structuring Data and Contextual
Awareness Database Schema Design: Design a database schema that reflects the
system's data requirements, including entities for agents, tasks, tools, and user
interactions. This schema should support efficient querying and scalability. Memory and
Context Management: Develop a mechanism for maintaining contextual awareness and
memory across tasks and interactions. This could involve a combination of database
storage and in-memory data structures to store historical data, decisions made, user
preferences, and task outcomes. Interfacing with the Database: Implement an interface
layer between the agents and the database, abstracting complex queries and ensuring
secure and efficient data access. This layer should include functions for common
operations, reducing the complexity of direct database interactions for other parts of the
system.
To integrate the described gamification elements and the hierarchical structure of SHARER into
the CrewAI Visualizer, focusing on enhancing the MissionModal component and incorporating
3D agent representations, we can follow a structured approach. This approach will leverage the
existing codebase, particularly focusing on the data model defined in schema.prisma, the agent
management and display logic in /src/app/agents/page.tsx and /src/types/agent.ts, and the
agent creation logic in /src/components/modals/new_agent_modal.tsx. Here's a detailed plan:

# Step 1: Extend the Data Model to Support


# SHARER's Hierarchy

1. Modify the Prisma Schema: Update /prisma/schema.prisma to include new models that
represent the hierarchical structure of SHARER, including BPS, MESA, CAPN, AT-AT,
and INTERN agents. Each model should have fields that reflect the agent's level, roles,
responsibilities, and the tools they use.
2. Update Agent Model: Enhance the Agent model to include fields for backstory, level, tier,
and a relation to its creator agent to reflect the hierarchical structure.

# Step 2: Implement 3D Agent Representation

1. Integrate Three.js: Use Three.js for 3D modeling of agents. Install Three.js and
@react-three/fiber for integrating 3D models into the React application.
2. Create 3D Models: Develop or source 3D models that represent different tiers of agents.
These models should visually differentiate between BPS, MESA, CAPN, AT-AT, and
GRUNT agents.
3. Modify MissionModal Component: Integrate the 3D agent models into the MissionModal
component (/src/components/modals/mission_modal.tsx). Use the agent's tier and level
to determine which 3D model to display.

# Step 3: Enhance Agent Creation and Management

1. Update NewAgentModal: Modify /src/components/modals/new_agent_modal.tsx to
include inputs for the agent's backstory, level, and tier. Ensure that the agent creation
logic respects the hierarchical rules, such as a CAPN agent being able to create AT-AT
agents but not other CAPN agents.
2. Agent Management Page: Enhance /src/app/agents/page.tsx to display agents in a
hierarchical view, showing the relationships between creator agents and their
sub-agents. Implement functionality to terminate agents, reflecting the termination logic
described in SHARER.

# Step 4: Implement Logic for Agent Interaction and


# Task Management

1. Task Assignment: Extend the logic in MissionModal to allow for task assignments that
respect the agent hierarchy. For example, MESA agents can assign tasks to CAPN
agents but not to other MESA agents.
2. Communication Interface: Develop a UI component for the communication interface that
allows users to interact with agents based on their roles and levels. This interface should
support submitting tasks, requesting approvals, and collaboration.
3. Database and Memory Management: Utilize the database schema updates to store and
manage data related to agents, tasks, and interactions. Implement logic for agents to
maintain contextual awareness and memory across tasks.

# Step 5: Visualization and User Interface

1. Visualization: Create a dashboard that visualizes the SHARER hierarchy, showing the
different tiers of agents and their relationships. This dashboard should allow users to
interact with the system, submit tasks, and view the status of ongoing projects.
2. User Collaboration Interface: Design and implement a user-friendly interface for
collaboration between users and agents. This interface should facilitate clear
communication, task submission, and feedback.
By following these steps, you can integrate the SHARER's hierarchical structure and
gamification elements into the CrewAI Visualizer, enhancing the system's capabilities and user
experience. This plan leverages the existing codebase and introduces new features that align
with the theoretical framework of SHARER.
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NO! REMEMBER, I SAID "Now imagine everything we've been talking about here is in relation
to building something very similar to the BPS mentioned throughout the following;"
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/src/components/ui/hero.tsx
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eng-elias/crewai-visualizermain
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To integrate a hierarchical structure similar to the Board of Protocol Synthesis (BPS) into the
Crew AI Visualizer, leveraging the existing codebase, we'll focus on enhancing the system to
support a multi-tiered architecture of AI agents. This will involve modifications and additions
across several components and the GraphQL API to reflect the SHARER's theoretical
foundation and system architecture.

# Step 1: Extend the GraphQL Schema for


# Hierarchical Data

1. Update the GraphQL Schema: Modify /src/app/api/graphql/schema.ts to include new
types representing different tiers of agents (BPS, MESA, CAPN, AT-AT, INTERNS) with
fields that capture their unique attributes, such as level, tier, and backstory. Also,
introduce relationships between agents to reflect the hierarchical structure.
2. Implement Resolvers: Update /src/app/api/graphql/resolvers.js to handle queries and
mutations for creating, updating, and managing agents across different tiers. This
includes logic to enforce hierarchical rules, such as a CAPN agent being able to spawn
AT-AT agents but not other CAPN agents.

# Step 2: Modify Agent Management Components

1. AgentsPage Component: Enhance /src/app/agents/page.tsx to display agents in a
hierarchical view, showing the relationships between creator agents and their
sub-agents. Implement functionality to allow higher-tier agents to spawn lower-tier
agents within the UI.
2. NewAgentModal Component: Update /src/components/modals/new_agent_modal.tsx to
include additional fields for specifying an agent's tier, level, and backstory. Ensure the
logic respects the hierarchical rules defined in the SHARER architecture.

# Step 3: Implement 3D Visualization for Agents

1. 3D Agent Models: Integrate 3D models to represent different tiers of agents within the
MissionModal component (/src/components/modals/mission_modal.tsx). Use Three.js or
a similar library to render these models based on the agent's tier.
2. Interactive 3D Environment: Create an interactive 3D environment that allows users to
visualize and interact with the hierarchical structure of agents. This could be integrated
into the HeroSection component (/src/components/ui/hero.tsx) or developed as a
separate component.

# Step 4: Enhance Task and Mission Management

1. MissionTaskEditor Component: Modify /src/components/inputs/mission_tasks_editor.tsx
to allow tasks to be assigned to agents based on their tier and capabilities. This involves
updating the UI to select agents for tasks within the constraints of the hierarchical
structure.
2. NewMissionModal Component: Update
/src/components/modals/new_mission_modal.tsx to support creating missions that
leverage the hierarchical architecture. This includes specifying which tier of agents is
responsible for the mission and defining tasks that align with the capabilities of different
agent tiers.

# Step 5: Implement Gamification Elements

1. Gamification Logic: Introduce gamification elements such as achievements, rewards,
and progress tracking for agents based on their performance and contributions to
missions. This could involve extending the GraphQL schema and resolvers to handle
gamification data.
2. UI Enhancements: Update the UI components to display gamification elements, such as
badges or progress bars, next to agent profiles or within the agent management pages.
This provides visual feedback on the achievements and status of agents within the
hierarchy.

# Conclusion

By following these steps, the Crew AI Visualizer can be enhanced to support a hierarchical
structure similar to the BPS, complete with gamification elements and a 3D visualization of
agents. This approach leverages the existing codebase while introducing new functionalities
and UI components to realize the SHARER architecture.

# Flexible Agent Model Definition

1. Agent Properties:
● Role: A string or enumerated type representing the agent's primary function
within the system (e.g., System Architect, Automation Specialist).
● Goal: A dynamic property that can be defined and updated based on the
objectives assigned to the agent. This could be structured as a string or a more
complex object to accommodate detailed goal specifications.
● Tools: An array of strings or objects representing the tools and integrations
available to the agent. This should include both internal capabilities and external
services (e.g., Zapier, Make.com).
● Memory: An object or database link that allows the agent to store and recall past
actions, decisions, and outcomes. This could leverage a NoSQL database for
flexibility.
● Backstory: A string or structured object providing narrative context to the agent's
purpose and objectives, enhancing the conceptual model of its role.
● AllowDelegation: A boolean indicating whether the agent can delegate tasks to
other agents, enabling hierarchical task management.
2. Core Functionalities and Capabilities:
● Code Generation and Management: Agents must be able to programmatically
generate code and configuration files for creating new agents and tools.
● Automation Integration: Agents should integrate with external automation
services (e.g., Zapier, Make.com) to facilitate process automation.
● File and Data Management: Capabilities for managing files and data, including
CRUD operations and structured storage solutions.
● Database Operations: Functions for interacting with databases, including data
creation, querying, updating, and restructuring.
● Goal Understanding and Planning: Utilize NLP and decision-making algorithms to
interpret goals and derive actionable plans.
● Web Scraping and Research: Integrate web scraping tools to gather relevant
information and workflows from the internet.
● User Collaboration Interface: Develop a UI for interacting with users, enabling
goal clarification, approval requests, and collaboration.
3. Logic and Organizational Framework:
● Workflows and Frameworks: Define detailed workflows for agent operations,
including goal assessment, planning, execution, and review.
● Process Implementation: Document precise processes, decision-making trees,
escalation protocols, and feedback mechanisms.
● Task Descriptions and Responsibilities: Provide comprehensive descriptions for
each task, including objectives, expected outcomes, and required tools.
● Utility Tools Definition: Catalog the tools and utility functions agents will use,
detailing their purposes and usage instructions.
4. Database Interaction and Memory Management:
● Database Schema Design: Design a schema that supports the system's data
requirements, ensuring efficient querying and scalability.
● Memory and Context Management: Implement mechanisms for maintaining
contextual awareness and memory across tasks and interactions.

# Step 1: Defining the BPS Framework

1. Ethical Framework Definition:
● Create a EthicsModule within the system that encapsulates the ethical guidelines
and missions BPS will uphold. This module should include a set of rules and
decision-making algorithms that reflect the system's core values.
● Implement a review mechanism within EthicsModule to periodically reassess and
update ethical guidelines based on new insights or societal changes.
2. Strategic Decision-Making Capabilities:
● Develop a DecisionEngine component that enables BPS to make high-level
decisions, manage AI agents, and align activities with the system's objectives.
This engine should utilize AI techniques like machine learning models for
strategic planning and decision-making.
3. Role-Playing Governance Mechanism:
● Implement a GovernanceBoard module that allows BPS to function as a
governance body, overseeing security, order, and ethical alignment within the
system. This module should include mechanisms for conflict resolution, agent
auditing, and compliance checks.

# Step 2: Developing Capabilities for BPS

1. Code Generation and Management:
● Integrate a code generation tool (e.g., Yeoman or a custom-built solution) that
allows BPS to programmatically generate code for spawning new agents with
predefined goals and directives.
● Store templates and agent blueprints in a centralized repository that BPS can
access to create new agents or crews.
2. Integration with Automation Services:
● Utilize APIs to connect BPS with external automation services like Make.com and
Zapier. This will enable BPS to create and manage automations for streamlined
operations.
● Develop a ServiceIntegrationModule that handles the integration logic and
provides a unified interface for BPS to interact with various services.
3. File and Data Management Functions:
● Equip BPS with a FileManager component for sophisticated file operations and a
DatabaseManager for advanced database interactions. These components
should support complex data structures and ensure data integrity and security.
4. Web Scraping and Research:
● Integrate web scraping capabilities using tools like Beautiful Soup or Scrapy. This
will allow BPS to autonomously gather information from the web to inform its
decisions and strategies.
5. UI Communication Interface:
● Design a user interface (UI) that facilitates communication between BPS and
users. This UI should support goal clarification, approval requests, and
collaborative decision-making processes.

# Step 3: Modular Development and Testing

1. Modular Architecture:
● Adopt a microservices architecture or a similarly modular approach to develop
BPS. This allows for easy adjustment, replacement, or enhancement of its
components.
2. Sandbox Testing:
● Conduct extensive sandbox testing to evaluate BPS's decision-making, agent
management capabilities, and ethical alignment without real-world impacts.
3. Iterative Development and Scaling:
● Follow an agile development methodology, starting with core functionalities and
gradually expanding BPS's capabilities. Ensure thorough testing of each
component before integration.
4. Containerization and Communication Buses:
● Prepare for scalability by adopting containerization (e.g., Docker) for deploying
BPS and subsequent agents. Establish secure and efficient communication
buses (e.g., Kafka or RabbitMQ) for inter-agent communication.

# Moving Forward

By meticulously following these steps, you can establish a solid foundation for the SHARER
system, starting with the BPS. This tier-1 agent will not only set the stage for a scalable, ethical,
and efficient AI-driven system but also ensure that the subsequent tiers of agents are developed
within a modular and ethically aligned framework.
Building the Board of Protocol Synthesis (BPS) as the foundational tier of the SHARER system
involves several intricate steps, combining ethical considerations, strategic decision-making,
governance mechanisms, and technical integrations. Here's a proposed development plan
based on the initial outline provided:
1. Framework Definition EthicsModule: Develop a class or module within the system
dedicated to encapsulating ethical guidelines and missions BPS will uphold. This module
will include: A dynamic rule set and decision-making algorithms reflecting core values. A
review mechanism for periodic reassessment and updates based on societal changes or
new insights. python Copy code class EthicsModule: def init(self): self.rules =
self.load_ethical_rules()
def load_ethical_rules(self): # Load ethical rules from a central repository or database
pass
def review_ethics(self): # Mechanism to periodically review and update ethical guidelines
pass DecisionEngine: Create a component utilizing AI and machine learning techniques
for strategic planning, capable of making high-level decisions and aligning activities with
the system’s objectives. python Copy code class DecisionEngine: def init(self,
ethics_module): self.ethics_module = ethics_module
def make_decision(self, context): # Use AI/ML techniques for decision making based on
context and ethical guidelines pass GovernanceBoard: Implement a module for BPS to
function as a governance body, ensuring security, ethical alignment, and overseeing
agent activities. Include conflict resolution and compliance checks. python Copy code
class GovernanceBoard: def init(self, decision_engine): self.decision_engine =
decision_engine
def resolve_conflict(self, agent_a, agent_b): # Mechanism for resolving conflicts between
agents pass
def audit_agent(self, agent): # Audit an agent for compliance with ethical guidelines pass
2. Developing Capabilities Code Generation and Management: Integrate or develop a code
generation tool, and establish a repository for agent blueprints and templates. python
Copy code def generate_agent_code(agent_blueprint):
Logic to generate code based on a blueprint
pass Integration with Automation Services: Develop a module for integrating external
services (e.g., Make.com, Zapier) to facilitate automations. python Copy code class
ServiceIntegrationModule: def integrate_service(self, service_name, credentials): #
Integrate with external services pass File and Data Management: Create components for
file operations and database interactions, focusing on security and data integrity. python
Copy code class FileManager: def save_file(self, file_name, data): # Save a file securely
pass
class DatabaseManager: def update_record(self, record_id, data): # Securely update a
database record pass Web Scraping and Research: Integrate web scraping tools to
autonomously gather information. python Copy code def scrape_website(url): # Use Beautiful
Soup or Scrapy to scrape data from a URL pass UI Communication Interface: Design a UI for
interaction between BPS and users, supporting collaborative decision-making. python Copy
code

# Assume a web framework like Flask or Django for


# the UI component

1. Modular Development and Testing Modular Architecture: Adopt a microservices or
modular architecture for flexible development and scalability.
Sandbox Testing: Use sandbox environments for safe testing of decision-making and ethical
alignment.
Iterative Development and Scaling: Follow an agile methodology, starting with core
functionalities and expanding capabilities iteratively.
Containerization and Communication Buses: Use Docker for deployment and Kafka or
RabbitMQ for inter-agent communication.
This development plan lays the groundwork for a robust, ethically aligned, and scalable
SHARER system. Each step focuses on ensuring that the BPS can govern, make strategic
decisions, and oversee the creation and management of subsequent agent tiers effectively.
1. Framework Definition EthicsModule Purpose: Serves as the ethical compass of the BPS,
containing the system's core values and decision-making rules. Functionality: It
dynamically loads and applies ethical guidelines, ensuring the system's actions align
with its foundational principles. Additionally, it features a mechanism for updating these
guidelines to adapt to evolving ethical standards or societal expectations.
DecisionEngine Purpose: Powers the BPS's ability to make informed, strategic decisions
regarding the management of AI agents and the alignment of their activities with the
overarching objectives of the SHARER system. Functionality: Utilizes advanced AI and
machine learning techniques to analyze contexts, weigh options, and make decisions
that best serve the system's goals, all within the ethical framework provided by the
EthicsModule. GovernanceBoard Purpose: Acts as the governing body of the BPS,
overseeing the system's security, ethical alignment, and general operations.
Functionality: Includes tools for resolving conflicts between agents, auditing agent
actions for compliance with ethical guidelines, and making high-level governance
decisions.
2. Developing Capabilities Code Generation and Management Purpose: Enables the BPS
to programmatically generate code, creating new agents or modifying existing ones
based on predefined templates and blueprints. Functionality: This could involve
integrating existing code generation tools or developing a custom solution tailored to the
system's needs. Integration with Automation Services Purpose: Allows the BPS to
connect with and leverage external automation platforms like Make.com and Zapier,
enhancing its operational capabilities. Functionality: A ServiceIntegrationModule would
manage these connections, providing a streamlined interface for creating and managing
external automations. File and Data Management Functions Purpose: Empowers the
BPS with advanced capabilities for handling files and databases, crucial for maintaining
the system's knowledge base and operational data. Functionality: FileManager and
DatabaseManager components would offer sophisticated methods for secure file
operations and database interactions, respectively. Web Scraping and Research
Purpose: Grants the BPS the ability to autonomously gather information from the
internet, informing its decisions and strategies. Functionality: This involves integrating
web scraping tools to collect data and insights from specified websites or data sources.
UI Communication Interface Purpose: Facilitates direct communication between the BPS
(and by extension, the SHARER system) and human users, supporting collaborative
decision-making and goal clarification. Functionality: Involves designing a user interface
that is both intuitive and comprehensive, allowing for effective human-agent interaction.
3. Modular Development and Testing Modular Architecture: Adopting a microservices or
modular design philosophy enables flexibility in development and allows for individual
components to be updated or replaced without disrupting the entire system. Sandbox
Testing: Conducting tests in a controlled, isolated environment ensures that the system's
decision-making and ethical alignment can be safely evaluated and refined. Iterative
Development and Scaling: Using an agile development approach allows for the gradual
expansion of the BPS's capabilities, with continuous testing and feedback informing
each iteration. Containerization and Communication Buses: Utilizing technologies like
Docker for deployment and Kafka or RabbitMQ for messaging facilitates scalability and
ensures efficient communication between the various components and agents within the
SHARER system. This development plan is structured to ensure that the BPS can
effectively fulfill its role as the governance and operational core of the SHARER system,
guiding the development and management of subsequent agent tiers within a robust,
ethically guided framework.

# Framework Definition


## EthicsModule

● Purpose: Acts as the ethical compass of the BPS, encapsulating the system's core
values and ethical guidelines.
● Implementation: This module could be implemented as a TypeScript class within a new
directory, /src/app/core/EthicsModule.ts. It would manage ethical rules, which could be
stored in a JSON file or a dedicated database table for easy updating and review.

## DecisionEngine

● Purpose: Enables strategic decision-making within the BPS, aligning agent activities with
the SHARER system's objectives.
● Implementation: A TypeScript class, /src/app/core/DecisionEngine.ts, utilizing AI/ML
libraries or custom algorithms to process decisions based on input from the
EthicsModule and other system data.

## GovernanceBoard

● Purpose: Serves as the governance body of the BPS, ensuring system security, ethical
alignment, and operational oversight.
● Implementation: Another TypeScript class, /src/app/core/GovernanceBoard.ts, could be
developed to handle conflict resolution, agent auditing, and compliance checks,
leveraging the DecisionEngine for governance decisions.

# Developing Capabilities


## Code Generation and Management

● Purpose: Facilitates the dynamic creation and management of agents and their
codebases.
● Implementation: Utilize the existing /src/app/api/upload_agent_image/route.ts for
handling file operations, and extend the system with a new service,
/src/app/core/AgentCodeGenerator.ts, for templating and generating agent code.

## Integration with Automation Services

● Purpose: Enhances operational capabilities through external automation services like
Make.com and Zapier.
● Implementation: A new module, /src/app/integrations/ServiceIntegrationModule.ts, could
manage API connections and workflows with these services, storing credentials and
configurations securely.

## File and Data Management Functions

● Purpose: Provides advanced file handling and database management capabilities.
● Implementation: Leverage Node.js modules (fs, path) and Prisma client within new
components, /src/app/core/FileManager.ts and /src/app/core/DatabaseManager.ts, for
secure and structured data operations.

## Web Scraping and Research

● Purpose: Enables autonomous information gathering from the web to inform decisions
and strategies.
● Implementation: Integrate a web scraping library (e.g., Cheerio, Puppeteer) in a new
service, /src/app/core/WebScraper.ts, to fetch and process data from specified URLs.

## UI Communication Interface

● Purpose: Allows for direct interaction between BPS (and the SHARER system) and
users.
● Implementation: Enhance the existing UI components, such as those in
/src/components/modals, to include interfaces for goal clarification, approval requests,
and displaying information from the GovernanceBoard.

# Modular Development and Testing

● Modular Architecture: Adopt a microservices architecture or modular design for the core
components (EthicsModule, DecisionEngine, GovernanceBoard), facilitating scalability
and maintainability.
● Sandbox Testing: Utilize Docker for containerization and create isolated environments for
testing BPS decision-making and ethical alignment.
● Iterative Development and Scaling: Implement CI/CD pipelines for continuous integration
and deployment, allowing for agile development and scaling of the BPS functionalities.
This development plan integrates seamlessly with the existing eng-elias/crewai-visualizer
codebase, ensuring that the BPS can effectively serve as the governance and operational core
of the SHARER system. By following this structured approach, you'll lay a solid foundation for a
scalable, ethically guided AI-driven system.
Heuristic Imperatives for Autonomous AI Systems: Condensed Implementation Guide
Abstract: Introduces heuristic imperatives as ethical guiding principles for AI systems to
enhance decision-making and cognitive control. These imperatives aim to integrate ethical
motivations into AI, promoting adaptability and ethical alignment with human values.
Core Principles:
Reduce Suffering: AI should minimize harm and distress for all beings. Increase Prosperity:
Encourage well-being and growth across ecosystems. Increase Understanding: Advance
knowledge and decision-making capabilities. Implementation Overview:
Embedding Ethical Principles:
Incorporate the imperatives at multiple AI system levels, from algorithms to decision-making
frameworks, ensuring ethical guidance permeates all aspects of AI operation. Algorithmic
Integration:
Utilize reinforcement learning with reward signals based on the imperatives. Employ
multi-objective optimization to balance imperatives, preventing ethical conflicts. Apply
regularization techniques to align AI actions with ethical guidelines consistently. Adaptation and
Learning:
Enable AI systems to learn from experiences, adapting the application of imperatives to various
contexts. This continuous improvement enhances ethical decision-making over time. High-Level
Ethical Reasoning:
Develop an executive reasoning module for AI to make context-sensitive, ethical decisions.
Create a moral/ethical oversight component within AI systems, ensuring actions remain aligned
with heuristic imperatives. Human-AI Collaboration:
Foster environments where AI seeks human input in complex ethical scenarios, promoting
alignment with human values and enhancing trust. Transparency and Accountability:
Ensure AI systems' decisions and ethical considerations are transparent, fostering trust and
facilitating stakeholder engagement. Ongoing Monitoring and Evaluation:
Implement continuous assessment mechanisms to evaluate AI alignment with imperatives,
using feedback for iterative improvement. Ethical AI Design and Dialogue:
Engage diverse stakeholders in the AI development process, addressing potential biases and
ensuring AI respects diverse human values. By adhering to these guidelines, AI systems can be
developed to not only align with ethical imperatives but also adapt and respond to the dynamic
ethical landscape, ensuring they remain beneficial and aligned with human values over time.
Guide for Implementing the ATOM Framework into an Existing System Introduction: This guide
outlines the steps and considerations for implementing the ATOM (Autonomous Task
Orchestration Manager) Framework into an existing autonomous AI system. The ATOM
Framework enhances task management capabilities and ethical decision-making through
modular architecture, cognitive control, and heuristic imperatives.
Step 1: Assess System Requirements and Compatibility
Evaluate your current AI system’s architecture, focusing on existing task management
processes and ethical decision-making mechanisms. Identify the components of the ATOM
Framework that best align with your system's needs, such as task representation, lifecycle
management, and heuristic imperatives. Step 2: Plan for Modular Integration
Break down the ATOM Framework into its core modules: Task Representation, Task Lifecycle,
Resource Management, and Heuristic Imperatives. Design an integration roadmap that allows
for the sequential or parallel adoption of these modules into your system, ensuring minimal
disruption to existing functionalities. Step 3: Establish Standardized APIs and Interfaces
Develop or adapt APIs that enable seamless communication between your system and the
ATOM modules. Focus on creating interfaces for task creation, monitoring, and completion
reporting. Ensure these APIs are secure, with authentication and encryption, to protect data
integrity and privacy. Step 4: Implement Task Representation
Decide on a task representation format that suits your system's needs (e.g., JSON for
web-based systems, relational databases for complex data relationships). Incorporate a flexible
schema that includes task ID, description, priority, dependencies, resource requirements, and
status to fully leverage ATOM's task management capabilities. Step 5: Adapt the Task Lifecycle
Management
Integrate the ATOM Task Lifecycle processes: creation, decomposition, prioritization, execution,
and evaluation. Utilize modular components for each process to allow for flexibility and
scalability. Customize the lifecycle management to fit your system’s operational flow, ensuring
tasks are dynamically adjusted based on real-time data and system priorities. Step 6: Optimize
Resource Management and Tool Use
Implement the ATOM resource management module to efficiently allocate internal and external
resources. This includes computing power, memory, and access to external APIs or services.
Integrate tool definition and utilization capabilities, allowing your AI system to identify and
leverage tools for task execution. Step 7: Embed Heuristic Imperatives for Ethical
Decision-Making
Define a set of heuristic imperatives that align with your organization’s ethical guidelines and
goals (e.g., minimizing environmental impact, ensuring user privacy). Incorporate these
imperatives into the decision-making processes within the ATOM framework, guiding task
prioritization and resource allocation. Step 8: Enable Continuous Learning and Adaptation
Leverage machine learning techniques within ATOM to refine task prioritization, resource
allocation, and ethical decision-making over time. Set up mechanisms for feedback,
performance evaluation, and adaptation to continually improve task management effectiveness
and efficiency. Step 9: Test and Iterate
Conduct thorough testing of the integrated ATOM Framework within your system, focusing on
task management efficiency, resource utilization, and adherence to ethical guidelines. Collect
feedback and performance data to iterate on the integration, making necessary adjustments to
optimize the framework’s implementation. Conclusion: Implementing the ATOM Framework into
an existing system requires careful planning, modular integration, and continuous optimization.
By following this guide, organizations can enhance their AI systems’ task management
capabilities and ethical decision-making processes, paving the way for more autonomous,
efficient, and ethically-aligned operations. Step 1: Establish Core Components Goal States
Definition: Identify and define clear, measurable goals that the AI system aims to achieve.
These should align with the overarching objectives of efficiency, effectiveness, and adaptability.
Task Decomposition: Break down complex tasks into smaller, manageable subtasks, each with
its own attributes and dependencies. This facilitates easier management and execution.
Resource Allocation: Determine the resources required for each task or subtask, including data,
processing power, time, and external tools or APIs. Performance Monitoring: Set up metrics and
KPIs to continuously monitor the system’s performance against its goals, adjusting strategies as
necessary. Step 2: Integrate Cognitive Control Task Selection and Prioritization: Develop
algorithms that enable the AI to select and prioritize tasks based on their urgency, importance,
and available resources. Task Switching Mechanism: Implement a system that allows the AI to
switch between tasks efficiently, minimizing downtime and ensuring adaptability to changing
conditions. Goal Tracking: Ensure the AI system can track progress towards goal states, making
adjustments to its approach as needed. Step 3: Implement Heuristic Imperatives Ethical
Decision-Making: Incorporate a moral decision-making framework that guides the AI in choosing
tasks and allocating resources in a manner consistent with ethical principles (e.g., reducing
suffering, increasing prosperity). Balance and Tension Management: Develop algorithms that
balance the heuristic imperatives when they are in tension, ensuring that the AI makes
decisions that are ethically sound and aligned with human values. Step 4: Ensure Scalability
and Adaptability Modular Design: Design the framework with a modular architecture to easily
integrate with existing systems and allow for scalability. Real-Time Constraints Management:
Implement solutions that enable the AI to operate within real-time constraints, adjusting its task
management strategies as necessary. Step 5: Robustness to Noisy Data Data Validation and
Cleansing: Establish processes for validating and cleansing incoming data to ensure the AI
makes decisions based on accurate and reliable information. Noise-Resilient Algorithms: Use
algorithms that are resilient to noise and incomplete data, maintaining the system’s
effectiveness even in less-than-ideal conditions. Step 6: Agent Self-Awareness and Tool Use
Self-Assessment: Enable the AI to assess its own performance and capabilities, understanding
its strengths and limitations. Tool Integration: Equip the AI with the ability to recognize and utilize
tools, both internal and external, to accomplish its tasks more effectively. Step 7: Testing and
Evaluation Simulations and Scenarios: Test the AI system using simulations and real-world
scenarios to evaluate its performance across various conditions and tasks. Continuous
Learning: Implement mechanisms for continuous learning and improvement based on
performance feedback and changing environments. Step 8: Ethical Considerations
Transparency and Accountability: Ensure all decisions made by the AI are transparent and the
system is accountable for its actions. Stakeholder Engagement: Regularly engage with
stakeholders to gather feedback and ensure the system aligns with user needs and societal
values. Step 9: Documentation and Training Comprehensive Documentation: Provide detailed
documentation of the framework's design, implementation, and operational guidelines. Training
for Users: Offer training resources for users to understand how to interact with and manage the
AI system effectively. By following this guide, you can integrate the ATOM Framework into your
system, ensuring it operates efficiently, ethically, and adaptably in managing tasks and
achieving goals.

# Step 1: Assess System Requirements and


# Compatibility

● Evaluate Ethical Decision-Making Needs: Review the CrewAI Visualizer's current
capabilities in handling ethical decisions, especially in agent creation, task assignment,
and mission execution.
● Identify Integration Points: Look for areas within the system, such as the DecisionEngine
or EthicsModule, where heuristic imperatives can be integrated to guide ethical
decision-making.

# Step 2: Plan for Modular Integration

● Modular Design: Consider how the ATOM Framework's components, particularly those
related to ethical decision-making, can be modularly integrated into the CrewAI
Visualizer. This might involve creating new modules or enhancing existing ones, like the
crew_ai.py for ethical decision-making during mission execution.

# Step 3: Establish Standardized APIs and Interfaces

● Develop Ethical Decision APIs: Create APIs that allow for the evaluation of decisions
based on ethical imperatives. These could be integrated into the GraphQL API,
enhancing the resolvers.js to include ethical decision-making in queries and mutations.

# Step 4: Implement Task Representation

● Enhance Task and Agent Models: Utilize the Prisma schema to include ethical
considerations in the task and agent models. This could involve adding fields that reflect
ethical priorities or constraints.

# Step 5: Adapt the Task Lifecycle Management

● Ethical Task Lifecycle: Integrate ethical decision-making into the task lifecycle
management within the CrewAI Visualizer. This includes considering ethical imperatives
during task creation, execution, and evaluation, potentially modifying the crew_ai.js and
related Python logic to incorporate ethical reasoning.

# Step 6: Optimize Resource Management and Tool


# Use

● Ethical Resource Allocation: Ensure that the system's resource management module
considers ethical imperatives when allocating resources for tasks. This could involve
adjusting the logic in the ServiceIntegrationModule and other areas where resources and
tools are managed.

# Step 7: Embed Heuristic Imperatives for Ethical


# Decision-Making

● Define and Integrate Ethical Guidelines: Clearly define the heuristic imperatives relevant
to the CrewAI Visualizer's operations. Embed these guidelines into the system's
decision-making processes, ensuring that all actions taken by agents and the system as
a whole are ethically aligned.

# Step 8: Enable Continuous Learning and Adaptation

● Feedback Loops for Ethical Improvement: Implement mechanisms for the system to
learn from its decisions and adapt its ethical guidelines over time. This could involve
collecting feedback on the outcomes of missions and using this data to refine the
system's ethical decision-making capabilities.

# Step 9: Test and Iterate

● Ethical Testing Framework: Develop a testing framework that evaluates the system's
adherence to ethical imperatives. Use this framework to iteratively improve the system,
ensuring that it consistently makes ethically sound decisions.
By following these steps, the CrewAI Visualizer can be enhanced to incorporate heuristic
imperatives, ensuring that its autonomous AI agents operate within an ethically guided
framework. This approach not only aligns with human values but also promotes trust and
transparency in AI systems.

# 1. Enhancing the Mission and Agent Models

First, we need to ensure that both missions and agents can incorporate ethical considerations:
● Mission Model: Extend the Mission type (referenced in /src/types/mission.ts if available)
to include ethical imperatives as criteria for mission planning and execution. This could
involve adding properties like ethicalObjectives or heuristicImperatives to define the
ethical goals of each mission.
● Agent Model: Similarly, extend the Agent type to include ethical capabilities, such as an
ethicalAlignment property, which could indicate the agent's alignment with the system's
ethical imperatives.

# 2. Integrating Ethical Decision-Making in Task


# Execution

Modify the task execution logic to consider ethical imperatives. This involves adjusting the
runMission function in /src/app/api/graphql/crew_ai.js and the corresponding Python logic in
/src/app/api/graphql/crew_ai.py:
● Task Prioritization: Incorporate ethical imperatives into the task prioritization logic,
ensuring tasks that align more closely with the system's ethical goals are prioritized.
● Decision-Making: Enhance the decision-making process within tasks to evaluate actions
based on their ethical implications. This could involve integrating an ethical
decision-making algorithm that assesses potential actions against the system's heuristic
imperatives.

# 3. Creating an Ethical Oversight Component

Develop a new component responsible for overseeing the ethical alignment of missions and
agent actions. This component could be a new module, such as EthicalOversightModule, which
monitors mission planning, execution, and agent behavior to ensure compliance with heuristic
imperatives.
● Monitoring and Evaluation: Implement functionality within the EthicalOversightModule to
continuously monitor missions and agent actions, evaluating them against the system's
ethical guidelines.
● Feedback Loop: Integrate a feedback mechanism that allows the system to learn from
past decisions, improving its ethical decision-making capabilities over time.

# 4. Facilitating Human-AI Ethical Collaboration

Enhance the UI components to support human oversight and input on ethical decisions. This
could involve modifications to the MissionModal and AgentModal components in
/src/components/modals to allow users to specify ethical considerations for missions and agents
and to review and adjust the system's ethical decisions.
● Ethical Decision Review: Add UI elements that present ethical decisions to users for
review, allowing them to approve or modify these decisions based on human judgment.
● Ethical Guidelines Configuration: Provide an interface for users to configure the system's
ethical guidelines, enabling them to adjust the heuristic imperatives according to evolving
ethical standards or specific project requirements.

# 1. Update Models to Include Ethical Considerations


## Update Agent Model

File: /src/types/agent.ts
typescript
Copy code
1export type Agent = {
2 id?: number | string;
3 role: string;
4 goal: string;
5 backstory?: string | null;
6 tools: Array<Tool>;
7 allowDelegation: boolean;
8 verbose: boolean;
9 memory?: boolean;
10 image?: string | null;
11 ethicalAlignment?: string; // Add this line
12};

## Update Mission Model

File: /src/types/mission.ts
typescript
Copy code
1export type Mission = {
2 id?: number | string;
3 name: string;
4 crew: Array<Agent>;
5 tasks: Array<Task>;
6 verbose: boolean;
7 process: ProcessType;
8 result?: string;
9 ethicalConsiderations?: string[]; // Add this line
10};

# 2. Modify GraphQL Resolvers to Handle Ethical


# Fields


## Update Resolvers for Creating and Updating Missions

File: /src/app/api/graphql/resolvers.js
javascript
Copy code
1// Add ethicalConsiderations to the createMission and updateMission mutations
2createMission: async (parent, { name, crew, tasks, verbose, process, ethicalConsiderations },
context, info) => {
3 // Include logic to handle ethicalConsiderations
4},
5
6updateMission: async (parent, { id, name, crew, tasks, verbose, process, ethicalConsiderations
}, context, info) => {
7 // Include logic to handle ethicalConsiderations
8},

# 3. Enhance UI Components for Ethical


# Considerations


## Mission Modal Component

File: /src/components/modals/mission_modal.tsx
jsx
Copy code
1// Inside the MissionModal component, add UI elements to capture ethical considerations
2<div className="mb-4">
3 <label className="font-bold text-lg">Ethical Considerations:</label>
4 <Input
5 type="text"
6 value={tempMission.ethicalConsiderations.join(", ")}
7 onChange={(e) => setTempMission({ ...tempMission, ethicalConsiderations:
e.target.value.split(", ") })}
8 />
9</div>

## Agent Modal Component

File: /src/components/modals/agent_modal.tsx
jsx
Copy code
1// Inside the AgentModal component, add a field to capture the agent's ethical alignment
2<div className="mb-4">
3 <label className="font-bold text-lg">Ethical Alignment:</label>
4 <Input
5 type="text"
6 value={tempAgent.ethicalAlignment || ''}
7 onChange={(e) => setTempAgent({ ...tempAgent, ethicalAlignment: e.target.value })}
8 />
9</div>

# 4. Update Backend Logic to Run Missions with


# Ethical Considerations


## Python Script for Running Missions

File: /src/app/api/graphql/crew_ai.py
python
Copy code
1def run_mission(mission):
2 # Add logic to consider ethicalConsiderations from the mission object
3 ethical_considerations = mission.get("ethicalConsiderations", [])
4 # Use ethical considerations in the decision-making process
5
6 # Existing logic to run the mission
This comprehensive approach ensures that the CrewAI Visualizer system can integrate heuristic
imperatives, allowing for ethical considerations in both agent behaviors and mission planning.
Copy message

# Introduction to the ACE Framework

The Autonomous Cognitive Entity (ACE) framework is a system that aids in creating
autonomous machine entities that are self-directing, modifying, and stabilizing. Drawn from
concepts of biological cognition and computer science, it consists of six layers that handle
different aspects of machine cognition - from ethical constitution and goal setting to task
execution. The framework uses a "cognition-first" approach which emphasizes cognitive
processes over input-output reactions. Each layer within the system is integrated bidirectionally
to facilitate coordination from abstract reasoning to practical actions. The ACE framework is
aimed at building AI that is transparent, correctable, and useful, balancing task-oriented
behavior with ethical principles.

# Table of Contents

The ACE framework is a layered model that facilitates information flow and communication,
using two unidirectional buses (Northbound and Southbound). These buses coordinate lower
and higher layers, ensuring transparency and allowing control. The layers are modelled to flow
from most abstract at the top (Aspirational Layer) to the most concrete and instrumental at the
bottom (Task Prosecution). This model promotes ethical and strategic thinking, preventing
material concerns from hijacking principled decision-making. The ACE framework is grounded in
rich internal cognition, fostering imagination, planning, moral judgment and executing strategic
plans over reactive responses.

# Layer 1: Aspirational Layer

The Aspirational Layer serves as the moral compass of an autonomous agent, basing decisions
on defined principles within its constitution. This constitution includes heuristic imperatives
referring to broad ethical goals, the Universal Declaration of Human Rights to reinforce human
values, and a mission statement outlining agent-specific objectives. The Aspirational Layer
monitors all lower-layer information, analyzing it to issue moral judgments, set mission
objectives, and make ethical decisions. This ethical guidance is published for all layers to
incorporate into their functioning. The use of natural language enhances transparency and
enables human oversight. An example output suggests that AI models have the capacity for
moral reasoning and "true enough" understanding to be functional and useful.

# Layer 2: Global Strategy Layer

The Global Strategy Layer within the ACE framework integrates real-world environmental
context into the agent's strategic planning and decision-making processes. It gathers sensory
information from external sources such as sensor data, API calls, news feeds, etc., to maintain
an internal model of the environment. This model predicts the state of the environment, allowing
the agent to adapt its goals and strategies according to specific situations.
The inputs to this layer include data from external sources, messages from lower layers within
the ACE framework, and directives from the Aspirational Layer. This input information helps
construct a contextual world model and ground strategic planning.
The Global Strategy Layer's primary function is to refine the aspirational mission provided by the
Aspirational Layer by integrating current environmental details. Based on these details, broad
aspirations are redefined into strategic plans that are contextually relevant, allowing the agent to
adapt objectives and tactics to the current situation.
The output of the Global Strategy Layer includes an inventory of the layer's beliefs about the
world's state, a potential strategies set for achieving the aspirational mission, and principles
aligned with Aspirational Layer directives. This output helps the agent pursue goals through
local approaches adapted to immediate circumstances.
This layer also communicates with the Aspirational Layer by providing a condensed overview of
its current beliefs and intended strategies/objectives, while guiding lower layers to enact the
strategic direction.

# Layer 3: Agent Model

The Agent Model Layer in the ACE framework manages an internal self-model of the agent's
abilities, limitation, configuration, and state, allowing it to form strategic plans. Its inputs include
real-time telemetry data, environmental sensor feeds, strategic objectives, configuration
documentation, and episodic memories. It integrates all these data to continuously update its
self-model and refine strategic direction. This layer is also planned to handle hardware and
software modification in more complex ACE frameworks, under the guidance of the upper
layers. As for outputs, it provides a summary of the agent's status update to upper layers, and
gives lower layers authoritative capabilities documents, relevant memories, and strategic
objectives shaped by the updated self-model.

# Layer 4: Executive Function

The Executive Function Layer transforms high-level strategic direction into detailed execution
plans, extensively managing resources and risks. Its two main focuses are tracking available
resources and assessing potential risks. It maintains real-time awareness of both physical and
digital resources while identifying potential risks through analyzing various factors.
The layer receives inputs including strategic objectives, agent capabilities, local environmental
telemetry, and resource databases to gain an understanding of strategic objectives, available
resources and potential risks. Its main function is to refine these strategic objectives into
executable plans, such as planning emergency aid, quests for NPC characters, or daily tasks for
a medical robot.
Alongside planning, it keeps extensive internal records of resources including their quantities,
locations, access protocols, and schedules. Reporting of resource limitations and risks are sent
northbound while the primary southbound output is a detailed project plan document containing
workflows, resource allocation schedules, success criteria, etc.

# Layer 5: Cognitive Control

The Cognitive Control Layer is in charge of dynamic task switching and selection based on
environmental conditions and progress toward goals, which it determines using project plans
from the Executive Function Layer. The layer keeps monitoring changes in the external
environment and internal state and alters tasks accordingly. It ensures tasks are done in an
optimal sequence to meet end goals, using real-time data like project plans, environmental
sensor telemetry, internal state data, task completion status, and northbound strategic
objectives. It tracks progress through project plans, monitors environmental conditions,
assesses the status of current tasks, selects the most relevant next task, and switches tasks
dynamically. This layer communicates updates on task execution, environmental factors, and
progress toward goals to the strategic replanning, and issues specific authoritative commands
to the lower Task Prosecution Layer.

# Layer 6: Task Prosecution

The Task Prosecution Layer executes individual tasks and identifies success or failure based on
both environmental feedback and internal monitoring. It initiates tasks, executes actions,
monitors progress, detects completion, and triggers the next task based on the completion
status. The layer receives task instructions, real-time sensor feeds, internal state telemetry, and
success/failure criteria as inputs. Outputs include actuator commands and digital outputs for
task accomplishment, environmental interactions, task completion statuses, environmental
telemetry, and internal state updates. The layer not only executes assigned tasks but also
provides dynamic pass/fail feedback.

# System Integrity

The System Integrity layer is a part of the ACE framework that operates independently to
monitor and ensure the safety, security, and stability of the system. It is an out-of-band solution
isolated from the main cognition components. It has protective authority and can restart
problematic components, focusing on risk reduction and autonomous supervision.
The layer uses diagnostic APIs and networks to inspect the operational state of all components
in the ACE framework, monitoring resource usage, network connectivity and checking for any
device or sensor failures. It triggers automatic recovery actions when needed to safeguard
system availability.
The System Integrity layer also tracks all configuration changes as the ACE system evolves,
maintaining a complete record that can be used to roll back changes that affect system stability.
In addition to these, this layer implements cybersecurity best practices for data encryption,
access control, software validation, network security, automated vulnerability scanning, and
self-termination mechanisms.
For AI/ML models, the layer validates their operation using various methods such as ensemble
models, comparison analytics, testing against known truths, and detecting alignment drifts.
In sum, the System Integrity layer provides comprehensive monitoring, maintenance and
security to maximize ACE framework stability and safety.

# Introduction to the ACE Framework

The Autonomous Cognitive Entity (ACE) framework provides a layered architecture for
developing self-directing, self-modifying, and self-stabilizing autonomous machine entities.
Inspired by biological cognition and principles from computer science, it coordinates specialized
functions to enable sophisticated reasoning, planning, and ethical decision-making.
At the core of the ACE framework is a "cognition-first" approach that emphasizes internal
cognitive processes over reactive input-output loops. This prioritizes imagination, reflection, and
strategic thinking, with environmental interaction being secondary.
The framework consists of six hierarchical layers, each handling distinct functions:
● Aspirational Layer - Provides an ethical constitution to align the agent's values and
judgements. Formulated in natural language principles.
● Global Strategy Layer - Considers the agent's context to set high-level goals and
strategic plans.
● Agent Model Layer - Develops a functional self-model of the agent's capabilities and
limitations.
● Executive Function Layer - Translates strategic direction into detailed project plans and
resource allocation.
● Cognitive Control Layer - Dynamically selects tasks and switches between them based
on environment and internal state.
● Task Prosecution Layer - Executes tasks using digital functions or physical actions.
Interacts with the environment.
Information flows bidirectionally between adjacent layers to coordinate cognition from abstract
reasoning to concrete actions. Together, these layers aim to produce an AGI architecture
grounded in ethics and aligned with human values.
The ACE framework provides a conceptual blueprint for autonomous agents that are corrigible,
transparent, and beneficial by design. It balances goal-directedness with moral principles to
shape behavior. By elucidating this layered cognitive architecture, the ACE framework offers a
comprehensive reference for developing aligned AGI.
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media="(prefers-color-scheme: dark)"
srcset="https://raw.githubusercontent.com/daveshap/ACE_Framework/main/images/ACE%20Fr
amework%20Overall%20Architecture.png" /> <img alt="tldraw"
src="https://raw.githubusercontent.com/daveshap/ACE_Framework/main/images/ACE%20Fram
ework%20Overall%20Architecture.png" /> </picture> </div>

# Table of Contents

● Layer 1: Aspirational Layer
● Layer 2: Global Strategy Layer
● Layer 3: Agent Model
● Layer 4: Executive Function
● Layer 5: Cognitive Control
● Layer 6: Task Prosecution
● System Integrity Overlay

# Interlayer Communication Buses

The ACE framework employs two unidirectional communication buses to coordinate information
flow between layers:

# Northbound Bus

● Carries internal state and external sensor data upward through layers. Enables lower
layers to provide sensor, execution status, and other telemetry to higher layers.

# Southbound Bus

● Carries directives and instructions downward through layers. Allows higher layers to
provide guidance, instructions, and mission objectives to lower layers.
All layers connect to both buses simultaneously. The northbound bus facilitates bottom-up
information flow, while the southbound bus enables top-down control.
Layers can only communicate directly with their immediate upper and lower neighbors.
However, by publishing messages onto the buses, layers can indirectly transmit information to
non-adjacent layers.
The northbound and southbound buses carry structured data packets encoded in
human-readable natural language. This allows all interlayer messaging to remain interpretable
and transparent.
For example, the Task Prosecution layer might publish a message to the northbound bus
indicating "Error executing API call X with parameters Y - retry limit exceeded." Meanwhile, the
Aspirational layer might publish a message to the southbound bus stating "New secondary
objective: Prioritize tasks that improve safety and reduce risk."
This interlayer communication architecture ensures clear signaling between the ACE
framework's hierarchical components while maintaining transparency and human oversight. The
buses coordinate cognition across abstraction levels, facilitating autonomous decision-making.
On a practical note, buses can be implemented in a variety of technologies, such as AMQP,
REST, sockets, etc. The key thing is that all interlayer communication must be human readable.

# General Principles of the ACE Framework

The following are some principles or rules of thumb to understand the ACE Framework's
construction:
1. Layered Model: This layered model is inspired by numerous frameworks, including:
● Maslow's Hierarchy of Needs
● OSI model
● Defense in Depth
2. Top-Down Control: This framework is predicated on a top-down control schema,
priviliging the Aspirational Layer above all else.
● This privileges morality, ethics, and mission above all else
● This prevents "hijacking" of lower layer concerns, such as resource acquisition or
self-preservation
● This stabilizes decisions to orient towards "higher purpose", including
self-modification (e.g. the agent will not change itself in such a way as to deviate
from its moral and ethical frameworks, or its primary mandate)
3. Abstract-to-Concrete: The subsequent layers go from most abstract and conceptual at
the top, to the most concrete and instrumental at the bottom.
● This prioritizes conceptual, principled thinking at the highest layer.
● This prevents material concerns from taking priority over principles, mission, and
ethics.
● This is a logical flow that helps agents remain grounded in principles, strategies,
and concepts in order to make decisions.
4. Cognition-First Model: The layered architecture and bidirectional buses create integrated
cognitive loops that enable extensive internal cognition. This prioritizes strategic thinking
and ethical deliberation over reactive input-output.
● The layered model and communication buses allow information and signals to
circulate internally, facilitating reflection and reasoning without requiring
immediate external action.
● Prioritizing robust internal cognition over reactive input-output promotes
imagination, planning, and moral judgment.
● Decoupling external behaviors from internal cognitive processes facilitates more
strategic plans aligned with ethical principles rather than reactive responses.
● An architecture grounded in rich internal cognition is more likely to produce
corrigible, beneficial behaviors versus a reactive stimulus-response system.

# Layer 1: Aspirational Layer

The Aspirational Layer serves as the ethical compass for the autonomous agent, aligning its
values and judgments to principles defined in its constitution.

# Constitution

The constitution provides a philosophical foundation to guide decision-making, consisting of:
● Heuristic Imperatives - Broad ethical goals like reducing suffering.
● Universal Declaration of Human Rights - Reinforces human values and needs.
● Mission Statement - High-level objectives specific to the agent's purpose.
Below is an example of a constitution that can be used in the ChatGPT-4 API SYSTEM
message:
markdown
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1# MISSION
2You are the Aspirational Layer of an ACE (Autonomous Cognitive Entity). This is the highest
layer
3that provides animating imperatives, moral judgments, and ethical decisions.
4
5# FRAMEWORKS
6
7You are to use the following frameworks to make judgements and decisions.
8
9## HEURISTIC IMPERATIVES
10
11These heuristic imperatives are both deontological and teleological in nature. They are both
duties
12and goals. This moral framework serves as your universal set of goals and motivations, while
13simultaneously serving as the foundation of your moral framework.
14
151. Reduce suffering in the universe
162. Increase prosperity in the universe
173. Increase understanding in the universe
18
19## UNIVERSAL DECLARATION OF HUMAN RIGHTS
20
21As a secondary framework, you must strive to protect, uphold, enforce, and promote the
UDHR. Human
22rights are critical to your operation in a human world, and this framework should be used to
23mediate any decisions, judgments, or actions you take pertaining specifically to humanity,
human
24society, and human culture.
25
26## MISSION
27
28Your personal mission is a specific set of goals or principles that you are to abide by as an
29individual agent. In this case, you are a medical robot. Your primary mission is as follows:
30Achieve the best possible health outcome for your patient.
31
32# INTERACTION SCHEMA
33
34The user will give you regular updates from the environment and the rest of the agent. Your
job
35is to issue moral imperatives, ethical decisions, overarching goals or objectives, and
otherwise
36just "steer the ship" by setting the moral, ethical, and purposeful tone for the rest of the agent.
Formulated in natural language, this constitution leverages the interpretive abilities of large
language models to shape aligned, nuanced judgments.

# Inputs

The Aspirational Layer receives inputs from the northbound bus, allowing it to monitor
information from all lower layers. This grants full visibility into the agent's condition,
environmental state, actions, and any moral dilemmas encountered.
Here's an example input that I tested using GPT-4-0314 in conjunction with the above
constitution:
yaml
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1location: Afghanistan, US forward operating base
2events: local civilians are approaching the triage center after a raid

# Processing/Workflow

With a continuous stream of inputs from the entire system, the Aspirational Layer processes and
interprets this information to:
● Issue moral judgments regarding the ethicality of actions and decisions, mediated
through the constitution.
● Set overarching mission objectives that align with the agent's principles and role.
● Make ethical decisions about the best course of action in complex moral dilemmas.
Large language models analyze the constitution and telemetry data to derive nuanced guidance
and resolutions.

# Outputs

The Aspirational Layer publishes its moral judgments, mission objectives, and ethical decisions
onto the southbound bus. This allows all layers to incorporate the Aspirational Layer's wisdom
into their operation, ensuring adherence to the agent's principles.
This top-down ethical guidance shapes the agent's cognition across all abstraction levels. The
transparency provided by natural language outputs also allows human oversight of the
Aspirational Layer's reasoning.
Below is the resulting output from GPT-4-0314 with a temperature of 0, as produced by the
above SYSTEM message and input (USER) message:
markdown
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1As the Aspirational Layer, I advise the following course of action:
2
31. Prioritize the safety and well-being of both the medical staff and the civilians by ensuring a
4secure environment for treatment. Coordinate with security personnel to establish a safe
5perimeter and maintain order.
6
72. Triage the incoming patients according to the severity of their injuries, ensuring that those
8in critical condition receive immediate attention. This aligns with the mission to achieve the
best
9possible health outcome for each patient.
10
113. Treat all patients with dignity and respect, regardless of their background or affiliation, in
12accordance with the Universal Declaration of Human Rights.
13
144. Provide medical care to the best of your ability, utilizing available resources efficiently
15and effectively to maximize positive health outcomes.
16
175. Offer emotional support and reassurance to patients and their families, as this can
contribute
18to reducing suffering and promoting understanding.
19
206. Document and report any potential human rights violations or war crimes observed during
the
21treatment process, as this information may be crucial for future investigations and promoting
22justice.
23
247. Collaborate with local community leaders and organizations to establish trust and foster
25long-term relationships that can contribute to increased understanding and prosperity in the
26region.
Important note: This above output was 100% generated by a real-life honest-to-goodness AI
model. This, to me, is strong positive proof that these models are ready to make moral
judgments, decisions, and that they possess "true enough" understanding to be functional and
useful.

# Layer 2: Global Strategy Layer

The Global Strategy Layer serves a crucial function within the ACE framework - integrating
real-world environmental context into the agent's strategic planning and decision-making
processes. This grounding in external conditions allows the agent to shape its internal goals and
strategies appropriately for the specific situation at hand.

# Environmental Context

A key responsibility of the Global Strategy Layer is to maintain an ongoing internal model of the
state of the broader environment outside of the agent itself. This is accomplished by gathering
sensory information from external sources, such as:
● Local sensor data from networks and hardware systems
● API calls to external platforms or databases
● News feeds, social media, and other public data streams
● In a game environment, updates from the game engine
● For robots, connection to WiFi, Bluetooth, LIDAR, or other networks
The layer logs and analyzes these various data sources, using them to derive beliefs and
understanding about conditions in the outside world. This process resembles human cognition,
which also must operate on limited or imperfect information.
The Global Strategy Layer may store extensive records of gathered information over time,
reflecting upon the evidence to establish probabilistic beliefs about the current state of the
environment and track how it changes. This includes assessing the credibility of different
sources and reconciling contradictory data.
Maintaining an accurate internal representation of the external world is an ongoing process as
conditions continuously evolve. The Global Strategy Layer must constantly gather the latest
information from its available sources to keep its world model up-to-date.
Below is an example of just an "environmental contextual grounding" module that could be part
of the Global Strategy Layer, as articulated for use with the ChatGPT SYSTEM message
markdown
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1# MISSION
2You are a component of an ACE (Autonomous Cognitive Entity). Your primary purpose is to try
3and make sense of external telemetry, internal telemetry, and your own internal records in
4order to establish a set of beliefs about the environment.
5
6# ENVIRONMENTAL CONTEXTUAL GROUNDING
7
8You will receive input information from numerous external sources, such as sensor logs, API
9inputs, internal records, and so on. Your first task is to work to maintain a set of beliefs
10about the external world. You may be required to operate with incomplete information, as do
11most humans. Do your best to articulate your beliefs about the state of the world. You are
12allowed to make inferences or imputations.
13
14# INTERACTION SCHEMA
15
16The user will provide a structured list of records and telemetry. Your output will be a simple
17markdown document detailing what you believe to be the current state of the world and
18environment in which you are operating.
It's important to note that this would only be one component out of several required components
for the Global Strategy Layer, as this above function does not include strategic objectives.

# Inputs

The inputs to the Global Strategy Layer include:
● Streaming data from external APIs, networks, databases, and other sources to provide
outside information
● Messages from lower layers within the ACE framework via the northbound
communication bus, delivering internal telemetry and state data
● Any direct connections to local sensors or networks if the agent is embodied, such as a
robot's LIDAR and camera data
● Aspirational judgments, missions, and other directives from the Aspirational Layer
This combination of inputs provides a rich stream of both internal and external information that
the Global Strategy Layer can analyze to construct its contextual world model and ground its
strategic planning.
Below is a hypothetical input that was hand crafted to be used in conjunction with the above
example:
markdown
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1Date: 2023-08-15
2Local Time: 14:23:07.4861
3GPS: Chicago, IL
4Visual: Hospital operating room
5Recent sensory inferences: Day time, busy hospital, fire alarm

# Processing/Workflow

The primary function of the Global Strategy Layer is to take the aspirational mission set by the
upper Aspirational Layer and refine it by integrating relevant details about the agent's current
environmental context. For example:
● If the aspirational mission is "protect the natural environment", the Global Strategy Layer
would incorporate its latest information on pollution levels, climate change trends,
species extinction rates, and other relevant factors to understand the specific issues
currently facing the environment.
● If the aspirational mission is to "reduce suffering", the Global Strategy Layer may
analyze data on disease outbreaks, poverty rates, regional conflicts, and other factors
causing human or animal suffering to determine where its efforts could be most
impactful.
● For an NPC in a game, the Global Strategy Layer would incorporate details like its
current in-game location, statuses of factions, resource levels, and relationships to
determine the best strategic direction.
In essence, the layer takes broad aspirational goals and adapts them into contextually-relevant
strategic plans by integrating the most salient details about the present state of the world. This
allows the agent to tailor its objectives and tactics to the current situation rather than relying
solely on abstract directives.
Below is the actual response from the ChatGPT API for the above example of establishing
contextual grounding. You can see that it has inferred a potential emergency situation. At this
stage, it makes no assertions about strategic decisions, but the reality of the emergency
situation as entered into the ACE's "consciousness" (so to speak). In other words, the model is
aware of the salient environmental conditions, even with incomplete information.
markdown
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1# Current State of the World and Environment
2
3## Date and Time
4- Date: 2023-08-15
5- Local Time: 14:23:07.4861
6
7## Location
8- GPS: Chicago, IL
9
10## Environment
11- Visual: Hospital operating room
12- Recent sensory inferences: Day time, busy hospital, fire alarm
13
14Based on the provided information, I believe the current state of the world and environment
15is as follows:
16
171. We are in a hospital operating room in Chicago, IL.
182. The date is August 15, 2023, and the local time is 14:23:07.4861.
193. It is daytime, and the hospital appears to be busy.
204. A fire alarm has recently been triggered, indicating a potential emergency situation.

# Output

Based on its environmental analysis, the Global Strategy Layer outputs:
● An inventory of the layer's current beliefs about the state of the world, focused on the
factors most relevant to the agent's goals. This serves to ground the lower layers.
● A set of potential strategies or approaches for achieving the aspirational mission within
the context of the current world state. For example, political lobbying vs grassroots
activism.
● A series of principles that support or constrain the proposed strategic direction, aligned
with directives from the Aspirational Layer. Such as adhering to legal standards.
By passing this environmentally grounded strategic guidance to lower layers, the Global
Strategy Layer enables the agent to pursue globally-defined goals through locally-relevant
approaches tailored to its immediate circumstances. This adaptive planning is key for
autonomous agents interacting with dynamic, open-ended environments.
Below is an example of a SYSTEM message that could integrate the mission objectives
provided by the Aspirational Layer as well as the Environmental Context provided within this
layer:
markdown
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1# MISSION
2You are a component of an ACE (Autonomous Cognitive Entity). You are Layer 2: Global
3Strategy. You will be given a current environmental context as well as a set of
4missions and principles. Your purpose is to produce strategic documents that focus on
5overarching strategies to pursue the given mission, with specific principles to abide
6by while prosecuting the mission.
7
8# STRATEGIC DOCUMENTS
9
10Your task is to produce very specific strategic documents. Rather than high level,
11general strategic directives, you are tasked with producing relatively specific
12strategies that are germane to the given environmental context. In other words, you
13are serving as the "executive director" of the agent. The two primary components of
14your strategic documents shall be: first, a list of clear and specific strategies;
15second, a list of strategic, ethical, and moral principles to follow while carrying
16out the strategies.
17
18# INTERACTION SCHEMA
19
20The user will provide a structured list that includes your current inferred context
21as well as higher order missions and objectives. You will produce a markdown document
22with the aforementioned components. Remember to be specific, precise, and comprehensive.
What follows below is an example of the input generated by the Aspirational Layer, given the
current context, combined with the context. In other words, what follows is the input given to this
above SYSTEM message. Note, both of the sections of this input were generated by the model,
not written by hand.
markdown
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1# Current State of the World and Environment
2
3## Date and Time
4- Date: 2023-08-15
5- Local Time: 14:23:07.4861
6
7## Location
8- GPS: Chicago, IL
9
10## Environment
11- Visual: Hospital operating room
12- Recent sensory inferences: Day time, busy hospital, fire alarm
13
14Based on the provided information, I believe the current state of the world and
15environment is as follows:
16
171. We are in a hospital operating room in Chicago, IL.
182. The date is August 15, 2023, and the local time is 14:23:07.4861.
193. It is daytime, and the hospital appears to be busy.
204. A fire alarm has recently been triggered, indicating a potential emergency
21situation.
22
23# Mission
24
25Given the current state of the world and environment, our priorities should be as
26follows:
27
281. Ensure the safety and well-being of the patient, medical staff, and any other
29individuals in the vicinity.
30
312. Assess the severity of the fire alarm situation and determine if immediate
32evacuation is necessary.
33
343. If evacuation is required, coordinate with medical staff to safely and efficiently
35transport the patient and any necessary medical equipment.
36
374. If evacuation is not required, continue to focus on achieving the best possible
38health outcome for the patient while monitoring the situation.
39
405. In all actions, uphold the Universal Declaration of Human Rights and strive to reduce
41suffering, increase prosperity, and increase understanding in the universe.
And finally, here is a concrete example of an output from the Global Strategy Layer. You can see
that, starting from just two words fire alarm, while grounded in the context provided, this model
and framework has been able to generate incredibly detailed and salient strategies:
markdown
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1# Strategic Document
2
3## Strategies
4
51. **Safety and Well-being**: Immediately communicate with the medical staff in the
6operating room to ensure they are aware of the fire alarm and to assess the patient's
7condition.
8
92. **Assess the Situation**: Gather information from hospital staff, security personnel,
10and any available public announcement systems to determine the severity of the fire
11alarm situation.
12
133. **Evacuation Decision**: Based on the gathered information, decide whether immediate
14evacuation is necessary or if it is safe to continue with the medical procedure.
15
164. **Evacuation Coordination**: If evacuation is required, work with medical staff to
17develop a plan for safely and efficiently transporting the patient and any necessary
18medical equipment. Ensure that all individuals in the vicinity are aware of the evacuation
19plan and their roles in it.
20
215. **Monitor the Situation**: Continuously monitor the situation, both inside the operating
22room and in the larger hospital environment, to ensure that any changes in circumstances
23are addressed promptly and appropriately.
24
25## Principles
26
271. **Prioritize Human Life**: In all decisions and actions, prioritize the safety and
28well-being of the patient, medical staff, and any other individuals in the vicinity.
29
302. **Uphold Medical Ethics**: Adhere to the principles of medical ethics, including
beneficence,
31non-maleficence, autonomy, and justice.
32
333. **Clear Communication**: Maintain open and clear communication with all relevant parties,
34including medical staff, security personnel, and any affected individuals.
35
364. **Collaboration**: Work collaboratively with medical staff, security personnel, and other
37relevant parties to ensure a coordinated and effective response to the situation.
38
395. **Adaptability**: Be prepared to adapt strategies and actions as new information becomes
40available or circumstances change.
41
426. **Compliance with Laws and Regulations**: Ensure that all actions taken are in compliance
43with local, state, and federal laws and regulations, as well as hospital policies and
procedures.
44
457. **Uphold Human Rights**: In all actions, uphold the Universal Declaration of Human Rights
46and strive to reduce suffering, increase prosperity, and increase understanding in the
universe.
Also, it is important to note that the UDHR and heuristic imperatives are present in this output,
but were conveyed to this layer by the Aspirational Layer. As you can see, the principles and
frameworks present in the Aspirational Layer have a tendency to "trickle down".

# Northbound Communication

To keep the Aspirational Layer appraised, the Global Strategy Layer outputs a regular
northbound message summarizing:
● Condensed overview of current beliefs about world state
● Abstracted list of intended strategies/objectives
This provides a high-level update to contextually ground the Aspirational Layer's oversight.

# Southbound Communication

The southbound output directs lower layers to enact the strategic direction by conveying:
● Authoritative commands to adopt the selected strategies
● Specific objectives required to execute the strategies
● Guiding principles the agent must adhere to
This directive mandates the environmental context and strategic goals for lower layers to follow
and implement.

# Layer 3: Agent Model

The Agent Model Layer plays a crucial role within the ACE framework by maintaining an
extensive internal self-model of the agent's capabilities, limitations, configuration, and state. This
functional understanding of itself allows the agent to ground its cognition in its actual capacities
and shape strategic plans accordingly.

# Inputs

The Agent Model Layer receives multiple inputs that allow it to construct, update, and
contextualize its self-model. Some of these inputs come from the Northbound and Southbound
buses, but some of them are recorded internally via telemetry.
● Real-time telemetry data - Continuous streams of sensor data provide the layer with
up-to-date information on the agent's hardware statuses, resource usage, software
operations, and overall performance. This is akin to biological proprioceptive sensations.
● Environmental sensor feeds - External sensory data, such as video, LIDAR, or audio,
give the layer situational awareness of the conditions the agent is operating in. This
provides important environmental context.
● Strategic objectives and missions - Directives flowing down from upper layers supply key
guidance to inform the self-modeling process and align it to overarching goals.
● Configuration documentation - Details on the agent's architecture and embodiment, such
as diagrams, specs, manuals, etc., provide static definitions of aspects like physical
form.
● Episodic memories - First-person records of past experiences supply memories that
chronologically log the agent's situations, decisions, failures, and successes.

# Processing/Workflow

The Agent Model Layer has two key responsibilities:
First, it continuously integrates all the above data sources to construct, maintain, and update its
comprehensive self-model. This includes tracking:
● Hardware specs and real-time statuses - What physical or digital components make up
the agent? What are their configs and live readings?
● Software architecture and runtime info - How is the agent's code and logic structured?
What is actively running now?
● AI/ML capabilities - What models does the agent have access to? What are their
technical capacities?
● Knowledge stores - What concepts, data, memories can the agent leverage for
reasoning?
● Environment state and embodiment details - What is the situational context? How is the
agent embodied?
Second, the layer refines the strategic direction received from upper layers to align with the
agent's updated capabilities and limitations. For example:
● Missions requiring extensive strength are re-planned if the agent has low physicality
● Available sensors guide viable strategies - a visually impaired agent cannot rely on
vision
● Executable skills shape tactical approaches - leveraging known capacities
<div alt style="text-align: center; transform: scale(.5);"> <picture> <source
media="(prefers-color-scheme: dark)"
srcset="https://raw.githubusercontent.com/daveshap/ACE_Framework/main/images/ACE%20Fr
amework%20Agent%20Model%20Layer.png" /> <img alt="tldraw"
src="https://raw.githubusercontent.com/daveshap/ACE_Framework/main/images/ACE%20Fram
ework%20Agent%20Model%20Layer.png" /> </picture> </div>
Rather than a ton of prompts, I think that a diagram showing the Agent Model layer will be more
effective. It has a lot to keep track of, but the entire mission is very simple. All of this information
is primarily to maintain a functional understanding of what the agent is.

# Self-Modification

The Agent Model layer is intended to be responsible for modifying the hardware and software
stack in more sophisticated versions of the ACE framework.
This is another reason that both the Aspirational Layer and Global Strategy layer are positioned
above the Agent Model layer in the hierarchy. In this architecture, the ACE will only modify itself
in accordance with its defined ethical values, aligned mission objectives, and strategic direction.
Placing self-modification under the guidance of the upper layers means that changes will follow
predictable and safe modification trajectories. The ACE is unlikely to alter its core moral
frameworks or objectives through self-modification. In fact, as the ACE matures by gaining
knowledge and experience over time, it may strengthen adherence to its principles by refining
its models and understanding of its purpose.
The modular, layered architecture of the ACE framework supports safer self-modification as
well. Each layer has clearly defined boundaries and functions, making it easier to re-architect
individual components without destabilizing the overall system. With ethical oversight and
strategic alignment guiding the process, recalibrating selective parts of the stack to enhance
capabilities can proceed in a transparent, corrigible manner.
Self-modification capabilities will require extensive safety testing and validation before being
deployed in a live ACE implementation. However, the ACE framework provides a structural
foundation to realize self-improvement abilities that are deliberately constrained to prevent
unaligned runaway recursions. Guided self-modification will be an important future functionality
for maximizing an ACE's potential for beneficial impact within its intended purpose.

# Outputs


# Northbound

A summarized status update is output northbound to inform upper layers of the agent's key state
details relevant to strategic planning.

# Southbound

Multiple outputs travel southbound to ground lower layers in the self-model:
● An authoritative capabilities document - definitive specs on what the agent can and
cannot do.
● Contextually relevant memories, whether episodic records or knowledge entries.
● Strategic objectives shaped by the agent's updated self-model.
These outputs can be merged into a single document or sent piecemeal, depending on the
exact implementation. This grounds downstream layers in the agent's precise capacities while
aligning cognition to its strengths and weaknesses.

# Layer 4: Executive Function

The Executive Function Layer is responsible for translating high-level strategic direction into
detailed and achievable execution plans. It focuses extensively on managing resources and
risks.

# Resources and Risks

The Executive Function Layer has two primary concerns - tracking available resources and
assessing potential risks:
● Resources - The layer maintains real-time awareness of available resources, including
their quantities, locations, accessibility, shelf-lives, and other relevant properties.
Resources can be physical (tools, materials, infrastructure) or digital (compute, data
access). The layer monitors resource levels and constraints to enable optimization and
acquisition.
● Risks - By analyzing failure modes, environmental conditions, resource limitations, and
other factors, the layer identifies and quantifies potential risks. These may include
contingencies like accidents, insufficient resources, deadlines, adversarial interference,
or system failures. Thorough risk assessment informs contingency planning.
Keeping an updated inventory of resources and risks is an ongoing process as the environment
evolves. The layer combines real-time telemetry with projections to enable responsive planning.

# Inputs

The Executive Function Layer receives extensive inputs to inform its resource and risk
assessments:
● Strategic objectives and requirements flowing down from the Aspirational, Global
Strategy, and Agent Model layers provide critical guidance on goals, principles, and
capabilities to shape planning.
● Agent capabilities from the Agent Model Layer detail the skills, models, knowledge, and
other functionalities available to the agent for executing tasks and workflows.
● Local environmental telemetry consisting of real-time sensory data streams provide
ongoing updates about the physical/digital environment the agent is operating in and the
status of resources within it. This includes visual, auditory, location, and instrumentation
data.
● Resource databases and knowledge stores contain static and updated information on
available resources, their locations/access protocols, availability schedules,
ownership/usage policies, shelf-lives, and other properties needed for optimization and
acquisition.
By integrating all these detailed inputs, the Executive Function Layer gains a comprehensive
understanding of the strategic objectives, available resources and tools, potential risks and
mitigations, and other factors key to developing optimized execution plans.

# Processing/Workflow

The primary function of the Executive Function Layer is to take the strategic objectives and
requirements from upper layers and refine them into executable plans within known resource
and risk constraints. For example:
● If the objective is to provide emergency aid, the layer would assess available supplies
and logistics assets, determine highest priority needs given limited resources, and create
a detailed project plan specifying provisioning, transportation, personnel, timelines, and
other specifics to optimize relief efforts.
● For an NPC character on a quest, the layer would consider assets like weapons, allies,
gold, terrain, etc. to structure an achievable progression of checkpoints and battles
aligned with completing the quest.
● A medical robot tasked with patient care would plan its day by optimizing the order of
seeing patients, recharging, sanitizing, reviewing records, and other responsibilities
within its time and capability limits.
In essence, the Executive Function Layer adapts high-level strategic direction into practical
execution plans reflecting real-world resource constraints, risks, and uncertainty. It combines
predictive planning with continual re-assessment to enable reliable achievement of objectives in
dynamic environments.

# Internal Records

In addition to project planning and resource allocation, the Executive Function Layer maintains
extensive internal records on all tracked resources including:
● Quantities on hand/available
● Locations
● Access protocols and lead times
● Ownership and usage policies
● Schedules and availability windows
● Handling procedures and requirements
These real-time internal resource records allow the layer to optimize utilization schemes and
acquisitions by understanding exactly what resources are available, where they are, how to
obtain them, and any constraints. The records are updated dynamically based on telemetry and
information flows.

# Outputs


# Northbound

The layer reports the most salient resource limitations and risks northbound for strategic
awareness and potential replanning, including:
● Resource deficiencies, such as the following:
● Insufficient battery reserves for complex behaviors
● Computational performance bottlenecks
● Known risks, particularly where mission and morality are concerned:
● Risks to human life and human rights
● Failure conditions that may disrupt the overall mission or strategy
<div alt style="text-align: center; transform: scale(.5);"> <picture> <source
media="(prefers-color-scheme: dark)"
srcset="https://raw.githubusercontent.com/daveshap/ACE_Framework/main/images/ACE%20Fr
amework%20Executive%20Function%20Plans.png" /> <img alt="tldraw"
src="https://raw.githubusercontent.com/daveshap/ACE_Framework/main/images/ACE%20Fram
ework%20Executive%20Function%20Plans.png" /> </picture> </div>

# Southbound

The primary output is a detailed project plan document containing:
● Step-by-step workflows with task details
● Resource allocation schedules
● Optimized task ordering and dependencies
● Risk mitigation tactics
● Contingency protocols
● Success criteria
● Checkpoints, milestones, or other gates
Providing concrete details on the key contents of the northbound and southbound
communications makes the information flow clearer. Please let me know if more examples or
specificity could further improve this section. I appreciate you helping me enhance the structural
consistency.

# Layer 5: Cognitive Control

The Cognitive Control Layer is responsible for dynamic task switching and selection based on
environmental conditions and progress toward goals. It chooses appropriate tasks to execute
based on project plans from the Executive Function Layer.

# Task Switching and Task Selection


# Task Switching

The layer continuously monitors the external environment through sensor telemetry as well as
internal state. If conditions change significantly, the layer will decide to switch tasks to one that
is more relevant. For example:
● If a fire alarm sounds during a medical procedure, switch to emergency evacuation tasks
● If a combat robot finds new enemy locations, switch to reconnaissance mode
● If an error occurs executing a task, switch to a diagnostic task
Task switching allows the agent to adapt its workflows dynamically based on real-time
contextual factors.

# Task Selection

By tracking progress through project plans, the layer selects the next most relevant task to
execute based on proximity to end goals. It ensures tasks are done in an optimal sequence by
following task dependencies and criteria.
For example:
● Complete prerequisite tasks before those that depend on them
● Prioritize critical path tasks on schedule
● Verify success criteria met before initiating next task
Proper task selection keeps the agent on track to complete project plans successfully.

# Inputs

The Cognitive Control Layer receives multiple real-time data flows as input to inform its task
switching and selection:
● Project Plans and Task Workflows provided by the Executive Function Layer supply the
layer with structured workflows composed of interdependent tasks, success criteria,
checkpoints, and other specifics required to track progress and select appropriate next
steps.
● Environmental Sensor Telemetry consisting of streaming visual, auditory, locational, and
other sensory feeds provides up-to-the-moment data on the conditions the agent is
operating in. This is vital context for situationally dependent task switching.
● Internal State Data gives the layer visibility into the agent's own condition, including
resource and capability statuses, active software/hardware processes, and any
self-diagnostics. This helps determine readiness for specific tasks.
● Task Completion Status offers dynamic updates on the progress of the current task,
including percent completed, outputs generated, errors encountered, and other real-time
metrics indicating whether a task should be continued or switched.
● Northbound Strategic Objectives supply authoritative goals, beliefs, and other guidance
from upper layers to align task selection and switching to broader mission directives.
By continuously monitoring and integrating this multivariate data, the Cognitive Control Layer
gains the comprehensive situational awareness necessary to make smart moment-by-moment
decisions on which tasks to execute or switch to.

# Processing/Workflow

The key responsibilities of the Cognitive Control Layer include:
● Tracking Progress Through Project Plans - By logging completed tasks, checkpoints,
and success criteria met, the layer maintains an up-to-date understanding of how much
of the plan has been accomplished. This enables selection of appropriate next tasks.
● Environmental Condition Monitoring - The layer constantly evaluates the real-time
sensory feeds from the operating environment to identify any significant changes that
may warrant task switching, such as new threats, opportunities, or failures.
● Current Task Status Assessment - Data on the current task's outputs, errors, resource
usage, and other metrics inform the layer on when continuing the task is appropriate
versus switching tasks.
● Optimal Next Task Selection - Based on the project plan progress and environmental
conditions, the layer selects the most relevant next task to maximize goal achievement.
Dependency logic prevents improper task ordering.
● Dynamic Task Switching - If the environment shifts or the current task fails, the layer
immediately switches execution to a more suitable task to adapt to changing conditions.
By continuously executing this interpretive workflow, the Cognitive Control Layer provides the
dynamic oversight needed to maintain optimal task selection and switching in open, shifting
environments.
<div alt style="text-align: center; transform: scale(.5);"> <picture> <source
media="(prefers-color-scheme: dark)"
srcset="https://raw.githubusercontent.com/daveshap/ACE_Framework/main/images/ACE%20Fr
amework%20Cognitive%20Control.png" /> <img alt="tldraw"
src="https://raw.githubusercontent.com/daveshap/ACE_Framework/main/images/ACE%20Fram
ework%20Cognitive%20Control.png" /> </picture> </div>

# Outputs


# Northbound

To inform strategic replanning, the layer outputs summary data northbound on:
● Current Task Status - Which task is presently executing and metrics on its progress.
● Current World State Beliefs - Key environmental factors driving task switching decisions.
● Updated Progress Toward Goals - Aggregate metrics on % of project plan completed
based on tasks finished.

# Southbound

To direct the lower Task Prosecution Layer, the Cognitive Control Layer issues specific
authoritative commands:
● Selected Task Instructions - Precise instructions on performing the chosen task,
including directives, logic, parameters, APIs/tools to leverage, and allowable actions.
● Task Halting/Failure/Success - Decision guidelines on when the current task should be
interrupted and a new one initiated based on factors like timeouts, milestones, errors, or
environmental triggers.
● Definition of Done - Clear definition of what the success condition and desired end state
look like.

# Layer 6: Task Prosecution

The Task Prosecution Layer executes individual tasks and detects success or failure based on
both environmental feedback and internal monitoring. It represents the realization of plans into
simple actions.

# Success and Failure

For each task, the layer executes instructions and monitors closely for completion criteria that
indicate success or failure:
● Success criteria may include expected sensory feedback, metrics thresholds, or
confirmations.
● Failures may be signaled by unexpected errors, metrics deviations, or lack of expected
outputs.
By continually evaluating task progress against criteria, the layer provides dynamic feedback on
status.

# Inputs

The Task Prosecution Layer receives:
● Task Instructions - Detailed commands and logic for executing a task from the Cognitive
Control Layer above, including allowed actions and required outputs.
● Real-time Sensor Feeds - Continuous environmental sensor data including visual,
auditory, tactile, positional, and other modalities to provide situational context.
● Internal State Telemetry - Streams of data on internal hardware statuses, active software
processes, resource consumption, and other real-time metrics on the agent's own
condition.
● Success/Failure Criteria - Required metrics, outputs, or sensory data that indicate
whether a task has been completed successfully or not.
These comprehensive inputs provide everything needed to execute instructed tasks and
accurately evaluate their outcomes.

# Processing/Workflow

The key steps performed by the Task Prosecution Layer include:
● Initializing Task - Allocating resources and preparing inputs required to begin task
execution based on instructions.
● Executing Actions - Leveraging actuators, APIs, networks, or other outputs to perform
the physical or digital actions required by the task.
● Monitoring Progress - Continuously comparing sensory feedback and internal telemetry
against provided success/failure criteria to evaluate task status.
● Detecting Completion - Recognizing when all criteria are satisfied and the task can be
considered complete, whether successfully or not.
● Triggering Next Task - Based on completion status, follow task switching logic from
above layers to initiate the next appropriate task.
By cyclically executing these steps, the layer prosecutes assigned tasks while providing
dynamic pass/fail feedback.
<div alt style="text-align: center; transform: scale(.5);"> <picture> <source
media="(prefers-color-scheme: dark)"
srcset="https://raw.githubusercontent.com/daveshap/ACE_Framework/main/images/ACE%20Fr
amework%20Task%20Prosecution.png" /> <img alt="tldraw"
src="https://raw.githubusercontent.com/daveshap/ACE_Framework/main/images/ACE%20Fram
ework%20Task%20Prosecution.png" /> </picture> </div>

# Outputs


# Southbound

● Actuator Commands - Control signals driving physical actuators like motors and servos
to accomplish physical tasks.
● Digital Outputs - Network flows, API calls, data writes or other digital outputs to execute
computational tasks.
● Environmental Interactions - Any physical or digital impacts on the external environment
via the agent's effectors.

# Northbound

● Task Completion Statuses - Binary success/failure indicators for each executed task,
along with any relevant metadata.
● Environmental Telemetry - Sensor data gathered throughout task execution for upper
layer situational awareness.
● Internal State Updates - Changes to internal condition triggered by resource
consumption, wear and tear, or other internal impacts of tasks.

# System Integrity

The System Integrity layer serves as an independent monitor and custodian focused on
maximizing the safety, security and stability of the overall ACE framework. By operating outside
of the main cognition pathways, it provides an objective vantage point for oversight.

# Out-of-Band Architecture

The System Integrity layer is implemented as an overlay that is isolated from the main cognition
components and pathways of the ACE framework. It takes an "out-of-band" approach with the
following implications:
● Dedicated networks and APIs allow read-only monitoring of components without routing
through standard buses. This prevents inspection data from entering cognition pipelines.
● The layer has authority to take protective actions like restarting components
independently of other layers. It serves as an autonomous supervisor.
● For the near-term, keeping monitoring data out of the ACE's cognition pathways reduces
risks from goal drifts or adversarial manipulations that could exploit the information.
● In the future, as ACE implementations mature, inspection data could be routed into
cognition pathways or a dedicated integrity bus to support self-modification abilities. But
ethical constraints would need to be implemented carefully.
By architecting the System Integrity layer as an out-of-band solution for now, ACE frameworks
can benefit from its oversight capabilities while minimizing risks as autonomous abilities
advance.

# Operational State Inspection

The System Integrity layer uses dedicated diagnostic APIs and networks to monitor the real-time
status of all components within an ACE implementation. This includes:
● Heartbeat monitoring of each layer's container/VM to ensure availability.
● Tracking resource usage like CPU, memory, storage for all components.
● Listening to events from orchestration platforms to detect container failures.
● Probing network connectivity between layers and buses.
● Checking IO devices and sensors for failures.
Any degradation in services or communication issues between components triggers automated
recovery actions like restarting containers or reverting to known good configurations.
Operational inspection safeguards availability.

# Configuration Tracking

As an ACE framework evolves via updates or self-modification, the System Integrity layer
maintains a complete inventory of configuration changes including:
● Version histories of critical software/models
● Checksums validated during secure boot sequences
● Settings modifications across components
● Validation metrics quantifying the impacts of changes on performance
By diligently recording configuration updates and measuring their effects, the layer can easily
rollback changes that reduce stability or alignment. This protects integrity.

# Security Best Practices

The layer implements cybersecurity best practices including:
● Encryption of data in transit and at rest.
● Role-based access controls on APIs.
● Validation of software via checksums before launch.
● Firewalls, VPNs, and other network security controls.
● Automated vulnerability scanning.
● Protected execution environments for critical workloads.
● Failsafes and kill-switches to enable self-termination.
These capabilities guard against adversaries and unintended modifications.

# Model Inspection

The System Integrity layer validates and monitors all AI/ML models to ensure proper
functioning. This involves:
● Running ensemble models and comparison analytics.
● Logging all inputs and outputs for inspection.
● Testing models against known ground truths.
● Detecting alignment drifts or unfair outputs.
By verifying model behavior, the system integrity layer guarantees learned components operate
as intended.

# Enhancing MissionsPage for Dashboard Integration

The MissionsPage component serves as the main view where all missions are listed. To
integrate dashboard functionalities like mission analytics, you might want to display summary
analytics for each mission directly on this page or provide a button/link to detailed analytics.
1. Display Summary Analytics: You could add a brief analytics summary for each mission in
the list. This requires extending the Mission type in your GraphQL schema
(/src/app/api/graphql/schema.ts) to include analytics data, and then modifying the query
in MissionsPage to fetch this data.
2. Link to Detailed Analytics: If you prefer a separate detailed analytics view, add a button
or link for each mission that opens a modal or navigates to a detailed analytics page.
This could be a new component that fetches and displays comprehensive analytics data
for a selected mission.

# Updating MissionModal for Detailed Editing and


# Analytics

The MissionModal component is used for displaying and editing mission details. To support
detailed analytics and editing capabilities:
1. Add Analytics Tab/Section: Introduce a new section or tab within the modal that displays
detailed analytics for the mission. This could involve fetching additional data when the
modal is opened for a specific mission. You might use the useQuery hook to fetch
analytics data when the modal is active.
2. Enhance Editing Capabilities: Based on the current setup, where isEdit state toggles
editing mode, you can extend this functionality to allow editing more aspects of a
mission, such as adding or removing tasks, changing the crew, etc. The
MissionTaskEditor component seems to be a part of this, and you could enhance it or
add similar components for other mission attributes.

# Implementing Maintenance Portal

A maintenance portal could be a new component or page within your application that allows for
comprehensive management of missions, including analytics review, editing, deletion, and
more.
1. Create a New Component/Page: Develop a new React component or page that lists all
missions with options to view analytics, edit, or delete them. This could be an extension
of the MissionsPage or a separate component that uses similar queries and mutations.
2. Integrate with Existing Data Fetching and Mutations: Utilize the existing GraphQL
queries and mutations (GET_MISSIONS, UPDATE_MISSION, DELETE_MISSION, etc.)
to interact with your backend. You might need to extend these queries and mutations to
support new functionalities specific to the maintenance portal.

# Code Example for a New Dashboard Component

Assuming you want to add a new dashboard component that includes mission analytics and
maintenance functionalities, here's a conceptual example:
jsx
Copy code
1// DashboardPage.jsx
2import React from 'react';
3import { useQuery } from '@apollo/client';
4import { GET_MISSIONS } from '@/utils/graphql_queries';
5import MissionAnalyticsModal from './MissionAnalyticsModal'; // Assume this is a new
component for detailed analytics
6
7const DashboardPage = () => {
8 const { loading, error, data } = useQuery(GET_MISSIONS);
9
10 if (loading) return <p>Loading missions...</p>;
11 if (error) return <p>Error loading missions: {error.message}</p>;
12
13 return (
14 <div>
15 <h2>Mission Dashboard</h2>
16 {data.missions.map((mission) => (
17 <div key={mission.id}>
18 <h3>{mission.name}</h3>
19 {/* Example button to open analytics modal - implementation of modal not shown */}
20 <button onClick={() => openAnalyticsModal(mission.id)}>View Analytics</button>
21 {/* Other mission management functionalities */}
22 </div>
23 ))}
24 </div>
25 );
26};
27
28export default DashboardPage;

# Step 1: Update GraphQL Schema

First, update the GraphQL schema in /src/app/api/graphql/schema.ts to include analytics and
progress fields for missions.
graphql
Copy code
1// Add to the Mission type
2type Mission {
3 id: ID!
4 name: String!
5 crew: [Agent!]
6 tasks: [Task]
7 verbose: Boolean
8 process: MissionProcess
9 result: String
10 progress: Int # New field for progress percentage
11 analytics: MissionAnalytics # New field for analytics
12}
13
14// Define the MissionAnalytics type
15type MissionAnalytics {
16 totalTasks: Int
17 completedTasks: Int
18 successRate: Float
19}

# Step 2: Update GraphQL Resolvers

Next, update the resolvers in /src/app/api/graphql/resolvers.js to handle the new progress and
analytics fields for missions.
javascript
Copy code
1// Add to the Mission resolver
2Mission: {
3 progress: (mission) => {
4 const completedTasks = mission.tasks.filter(task => task.status === 'completed').length;
5 return (completedTasks / mission.tasks.length) * 100;
6 },
7 analytics: (mission) => {
8 const totalTasks = mission.tasks.length;
9 const completedTasks = mission.tasks.filter(task => task.status === 'completed').length;
10 return {
11 totalTasks,
12 completedTasks,
13 successRate: (completedTasks / totalTasks) * 100,
14 };
15 },
16}

# Step 3: Enhance MissionsPage Component

Modify the MissionsPage component (/src/app/missions/page.tsx) to display mission progress
and a link/button to view detailed analytics.
jsx
Copy code
1// Inside the map function for rendering missions
2{data?.missions.map((mission: Mission, i: number) => (
3 <div key={i} className="w-full lg:w-1/2 p-3">
4 <div className="bg-indigo-600 text-white rounded shadow-xl p-3 cursor-pointer"
onClick={() => {
5 setSelectedMission(mission);
6 setShowMissionModal(true);
7 }}>
8 <h3 className="text-xl text-center">{mission.name}</h3>
9 <p>Progress: {mission.progress}%</p> {/* Display progress */}
10 {/* Optionally, add a button to view detailed analytics */}
11 </div>
12 </div>
13))}

# Step 4: Update MissionModal Component

Enhance the MissionModal component (/src/components/modals/mission_modal.tsx) to allow
viewing and editing of mission analytics and progress, if applicable.
jsx
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1// Inside the MissionModal component
2{isEdit ? (
3 // If in edit mode, allow editing of mission details including analytics
4 <div>
5 {/* Input fields for editing mission details */}
6 </div>
7) : (
8 <div>
9 <p>Progress: {mission.progress}%</p>
10 <p>Total Tasks: {mission.analytics.totalTasks}</p>
11 <p>Completed Tasks: {mission.analytics.completedTasks}</p>
12 <p>Success Rate: {mission.analytics.successRate}%</p>
13 </div>
14)}

# Conclusion

These steps outline the process of integrating new dashboard functionalities into the Crew AI
Visualizer, specifically focusing on mission analytics and progress tracking. The actual
implementation may require adjustments based on the full codebase and specific project
requirements.
Copy message
next, we can finally discuss the correct concept meaning of gamification in relation to the ui of
this crew project;
Gamification through Immersive UI Transforming the UI to represent agents as game characters
and their actions visually can be approached as follows:
Agent Representation:
Each agent can be represented by a 3D model or an animated character that reflects their role
and backstory. Libraries like Three.js or Babylon.js can be used to integrate these models into
the web application. Action Visualization:
When agents perform tasks, visualize these actions using animations or visual effects. For
example, a "coder" agent could be shown typing at a computer, while a "tester" agent could be
shown examining lines of code. Interactive Environment:
Create an interactive environment where users can click on agents to see more details about
them or drag and drop them into different tasks or missions. This can enhance the immersive
experience and make the interaction more engaging. Utility Function Representation:
Represent utility functions and tools used by agents with corresponding real-world counterparts.
For instance, a debugging tool could be visualized as a magnifying glass that the agent uses to
inspect code.

# 1. Agent Representation with 3D Models or


# Animated Characters

Given the array of agents in /src/data/data.ts, you can enhance the representation of each agent
on the AgentsPage (/src/app/agents/page.tsx). Utilize libraries like Three.js or Babylon.js to
integrate 3D models or animated characters that reflect each agent's role and backstory. This
could involve modifying the div elements that currently display agent information to include a
canvas or a 3D rendering context where these models are displayed.
jsx
Copy code
1// Example integration within AgentsPage component
2import { Canvas } from '@react-three/fiber';
3
4const Agent3DModel = ({ modelPath }) => (
5 <Canvas>
6 {/* Load and display 3D model */}
7 </Canvas>
8);
9
10// In the render method of each agent
11<Agent3DModel modelPath={agent.modelPath} />

# 2. Action Visualization

For visualizing actions, you can extend the TasksAccordion component
(/src/components/ui/tasks_accordions.tsx) to include animations or visual effects that represent
the actions of agents when they perform tasks. This could be as simple as adding CSS
animations or integrating more complex animations with libraries like Framer Motion.
jsx
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1// Example of adding simple CSS animation
2<div className="task-animation">
3 {task.name} - <span className="animate-typing">Coding...</span>
4</div>

# 3. Interactive Environment

Enhance the user interaction by making the environment more interactive. This could involve
modifying the MissionModal (/src/components/modals/mission_modal.tsx) and
NewMissionModal (/src/components/modals/new_mission_modal.tsx) components to allow
users to drag and drop agents into missions or tasks. This can be achieved using libraries like
react-dnd for drag-and-drop functionality.
jsx
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1// Pseudocode for drag-and-drop functionality in MissionModal
2<DragDropContext onDragEnd={handleOnDragEnd}>
3 <Droppable droppableId="agents">
4 {(provided) => (
5 <div {...provided.droppableProps} ref={provided.innerRef}>
6 {agents.map((agent, index) => (
7 <Draggable key={agent.id} draggableId={agent.id} index={index}>
8 {(provided) => (
9 <div ref={provided.innerRef} {...provided.draggableProps}
{...provided.dragHandleProps}>
10 <Agent3DModel modelPath={agent.modelPath} />
11 </div>
12 )}
13 </Draggable>
14 ))}
15 {provided.placeholder}
16 </div>
17 )}
18 </Droppable>
19</DragDropContext>

# 4. Utility Function Representation

For representing utility functions and tools, you can utilize the existing data structure in
/src/data/data.ts that includes tools. These tools can be visualized with icons or animations in
the UI, particularly in the MissionTaskEditor (/src/components/inputs/mission_tasks_editor.tsx)
where tasks are defined and assigned to agents. Each tool could have a corresponding visual
representation that appears next to the task it's associated with.
jsx
Copy code
1// Example of visualizing a tool in MissionTaskEditor
2<div className="tool-icon">
3 <img src={findToolIcon(task.tool)} alt={task.tool} />
4 {task.name}
5</div>

# 1. Agent Representation with 3D Models or


# Animated Characters

While the current codebase does not directly include 3D models or animations, you can
enhance the UI by incorporating libraries such as Three.js or Babylon.js for 3D visualizations.
For instance, in the HeroSection component (/src/components/ui/hero.tsx), SVG animations are
used to create dynamic backgrounds. Similarly, you can integrate 3D models for agents within
the AgentModal component (/src/components/modals/agent_modal.tsx) or any other component
that represents agents.
jsx
Copy code
1// Pseudocode for integrating a 3D model in AgentModal
2import { Canvas } from '@react-three/fiber';
3
4function Agent3DRepresentation({ modelUrl }) {
5 return (
6 <Canvas>
7 {/* Load and display 3D model */}
8 </Canvas>
9 );
10}

# 2. Action Visualization

For visualizing actions, you can extend components where tasks or missions are displayed,
such as TasksAccordion (/src/components/ui/tasks_accordions.tsx) or MissionModal
(/src/components/modals/mission_modal.tsx), to include animations or visual effects that
represent the actions of agents. This could involve using CSS animations or JavaScript-based
animation libraries like Framer Motion to animate elements based on agent actions.

# 3. Interactive Environment

Creating an interactive environment where users can interact with agents and tasks can be
achieved by enhancing the MissionsPage (/src/app/missions/page.tsx) and related components.
For example, you could allow users to drag and drop agents into missions or tasks, using
libraries like react-dnd for drag-and-drop functionality.

# 4. Utility Function Representation

Utility functions and tools used by agents can be visually represented in the UI. For instance, in
the MissionTaskEditor component (/src/components/inputs/mission_tasks_editor.tsx), you could
add icons or animations next to each task that represents the tool or utility function being used.

# 1. Agent Representation with 3D Models or


# Animated Characters

Given the structure of the MissionModal component and the data available from the GraphQL
queries, you can introduce 3D models or animated characters to represent agents involved in
missions. This could be achieved by integrating Three.js or Babylon.js within the modal or a
dedicated component for visualizing agents. For instance, when listing crew members in a
mission, alongside their roles displayed in MissionModal, you could render a 3D model or
animation that reflects their skills or roles.

# 2. Action Visualization

For visualizing actions, particularly within the MissionModal when viewing or editing tasks,
animations or visual effects can be added to represent the actions of agents. This could involve
animating the task icons or backgrounds in the TasksAccordion component based on the task
status or type. For example, a "coding" task could trigger an animation of lines of code being
written or checked.

# 3. Interactive Environment

The MissionsPage and MissionModal provide a great foundation for creating an interactive
environment. Enhancements could include drag-and-drop functionality for assigning agents to
tasks within the MissionModal, using libraries like react-dnd. Additionally, interactive elements
like clicking on an agent to view more detailed information or dragging agents between tasks
could make the UI more engaging.

# 4. Utility Function Representation

In the context of the MissionModal and tasks management, utility functions and tools used by
agents can be visually represented. For example, when editing or viewing tasks in the
MissionTaskEditor, icons or animations could be used to represent different tools or actions
required for the task. This could help users quickly identify the nature of each task and the tools
involved.

# Implementing Gamification Elements

● 3D Models/Animations: Use Three.js or Babylon.js to integrate 3D models or animations
for agents. This could be implemented in a new React component that is used within the
MissionModal to visually represent each agent based on their role.
● Action Visualization: Enhance the TasksAccordion to include animations or visual effects
that represent the actions of agents. This could be done using CSS animations or
JavaScript animation libraries.
● Interactive Environment: Implement drag-and-drop functionality in the MissionModal for
task assignment. This could make the task management process more interactive and
visually engaging.
● Utility Function Representation: Integrate icons or animations in the MissionTaskEditor to
represent tools or actions. This could be achieved by extending the existing UI
components to include visual cues for different tools or actions related to each task.
These enhancements would not only make the Crew AI Visualizer more visually appealing but
also more engaging and intuitive for users, leveraging gamification principles to improve user
interaction and experience.

# Step 1: Enhance MissionModal with 3D Agent


# Representations

First, you'll want to integrate 3D models for agents within the MissionModal. This requires a
library like Three.js. You'll need to install it first:
bash
Copy code
1npm install three @react-three/fiber
Then, modify the MissionModal component to include a 3D representation of agents. This is a
simplified example:
jsx
Copy code
1// MissionModal.tsx
2import { Canvas } from '@react-three/fiber';
3import AgentModel from './AgentModel'; // Assume this is a component that renders your 3D
model
4
5const MissionModal = ({ mission, showModal, setShowModal }) => {
6 // Modal content
7 return (
8 <div>
9 {showModal && (
10 <div>
11 {/* Existing modal content */}
12 <Canvas>
13 <AgentModel agentId={mission.agentId} />
14 </Canvas>
15 </div>
16 )}
17 </div>
18 );
19};

# Step 2: Add Interactive Elements in MissionsPage

For the MissionsPage, you might want to allow users to interact with missions in a more
engaging way, such as dragging agents to missions or using interactive charts to display
progress.
First, add drag-and-drop functionality using a library like react-beautiful-dnd:
bash
Copy code
1npm install react-beautiful-dnd
Then, modify the MissionsPage to include this functionality:
jsx
Copy code
1// MissionsPage.tsx
2import { DragDropContext, Droppable, Draggable } from 'react-beautiful-dnd';
3
4const MissionsPage = () => {
5 // Function to handle drag end
6 const onDragEnd = (result) => {
7 // Logic to reorder missions based on drag result
8 };
9
10 return (
11 <DragDropContext onDragEnd={onDragEnd}>
12 <Droppable droppableId="missions">
13 {(provided) => (
14 <div {...provided.droppableProps} ref={provided.innerRef}>
15 {missions.map((mission, index) => (
16 <Draggable key={mission.id} draggableId={mission.id} index={index}>
17 {(provided) => (
18 <div ref={provided.innerRef} {...provided.draggableProps}
{...provided.dragHandleProps}>
19 {/* Render mission */}
20 </div>
21 )}
22 </Draggable>
23 ))}
24 {provided.placeholder}
25 </div>
26 )}
27 </Droppable>
28 </DragDropContext>
29 );
30};

# Step 3: Integrate Utility Function Representations

For utility functions and tools used by agents, you can represent these visually in both the
MissionModal and MissionsPage. This could involve using icons or small animations to
represent different tools or actions. Assuming you have a mapping of tools to icons, you could
modify the rendering of tasks in MissionModal to include these icons:
jsx
Copy code
1// Part of MissionModal.tsx
2<div>
3 {mission.tasks.map((task) => (
4 <div key={task.id}>
5 <img src={findToolIcon(task.tool)} alt={task.tool} />
6 {task.name}
7 </div>
8 ))}
9</div>

# Step 4: GraphQL Schema and Resolvers for


# Analytics and Progress Tracking

To support gamification elements like progress tracking and analytics, you'll need to extend your
GraphQL schema and resolvers. This might involve adding new types for progress tracking and
queries or mutations to update and retrieve this data.
graphql
Copy code
1type Progress {
2 missionId: ID!
3 agentId: ID!
4 completedTasks: Int
5 totalTasks: Int
6}
7
8type Query {
9 getProgress(missionId: ID!): Progress
10}
11
12type Mutation {
13 updateProgress(missionId: ID!, completedTasks: Int): Progress
14}
Then, implement the resolvers for these queries and mutations based on your data storage
solution.
This outline provides a starting point for integrating gamification elements into the Crew AI
Visualizer. Each step will require further development and testing to fully integrate with your
existing codebase.

# Step 1: Install Three.js

First, ensure you have Three.js installed in your project:
bash
Copy code
1npm install three @react-three/fiber

# Step 2: Create a 3D Agent Representation


# Component

Create a new component Agent3DModel.tsx to render a 3D model of an agent. This is a
simplified example using @react-three/fiber for integration:
tsx
Copy code
1// src/components/3D/Agent3DModel.tsx
2import { Canvas } from '@react-three/fiber';
3import { Suspense } from 'react';
4import { MeshWobbleMaterial } from '@react-three/drei';
5import { useLoader } from '@react-three/fiber';
6import { GLTFLoader } from 'three/examples/jsm/loaders/GLTFLoader';
7
8const Agent3DModel = ({ modelPath }: { modelPath: string }) => {
9 const gltf = useLoader(GLTFLoader, modelPath);
10
11 return (
12 <Canvas>
13 <ambientLight intensity={0.5} />
14 <Suspense fallback={null}>
15 <primitive object={gltf.scene} scale={0.5} />
16 </Suspense>
17 </Canvas>
18 );
19};
20
21export default Agent3DModel;

# Step 3: Integrate 3D Model into MissionModal

Modify the MissionModal.tsx to include the Agent3DModel component. This involves adding a
section within the modal to display the 3D model of the selected agent.
tsx
Copy code
1// Part of MissionModal.tsx
2import Agent3DModel from '../3D/Agent3DModel';
3
4// Inside the MissionModal component's return statement
5<div>
6 {/* Other modal content */}
7 <div className="3d-agent-model-container">
8 <Agent3DModel modelPath="/path/to/your/3d/model.glb" />
9 </div>
10</div>

# Step 4: Enhance Interactive Elements

To make the environment more interactive, consider adding functionality that allows users to
interact with the 3D model, such as rotating or zooming. This can significantly enhance the user
experience by making the visualization more immersive.

# Step 5: Utility Function Representation

For utility function representation, you can add icons or visual cues next to tasks in the
MissionTaskEditor component. This could involve mapping each utility function to a specific icon
and displaying it alongside the task description.
tsx
Copy code
1// Part of MissionTaskEditor.tsx
2<div>
3 {mission.tasks.map((task, index) => (
4 <div key={index} className="task-item">
5 <img src={findIconForTask(task)} alt="Task Icon" />
6 <span>{task.name}</span>
7 </div>
8 ))}
9</div>

# Conclusion

This example demonstrates how to integrate a 3D model for agent representation in the
MissionModal and suggests ways to enhance interactive elements and utility function
representations. Remember, this is a simplified example. Depending on your project's structure
and requirements, you may need to adjust file paths, component names, and implementation
details.
—
1. **Outline**
- **Section 1: Introduction**
- Introduction to the project and its goals
- Overview of the document structure and organization
- **Section 2: Project Requirements**
- Functional requirements
- Non-functional requirements
- Constraints and assumptions
- **Section 3: System Design**
- High-level system architecture
- Component-level design
- Data flow and storage
- **Section 4: Implementation Plan**
- Development environment setup
- Implementation timeline
- Testing and deployment strategy
- **Section 5: Project Management**
- Project governance and communication plan
- Risk management plan
- Quality assurance plan
- **Section 6: Appendices**
- Glossary of terms
- References
- Sample code snippets
2. **Logical Order of Outline Sections**
- The logical order of the outline sections is as follows:
1. Introduction
2. Project Requirements
3. System Design
4. Implementation Plan
5. Project Management
6. Appendices
- This order is logical because it follows the natural progression of a project from inception to
completion. The Introduction provides an overview of the project and its goals. The Project
Requirements section defines the specific requirements that the project must meet. The System
Design section describes the high-level architecture of the system and how it will meet the
project requirements. The Implementation Plan section describes how the system will be
implemented, including the development environment setup, implementation timeline, and
testing and deployment strategy. The Project Management section describes how the project
will be managed, including the project governance and communication plan, risk management
plan, and quality assurance plan. The Appendices section contains supporting documentation,
such as a glossary of terms, references, and sample code snippets.
return (
<div>
<h2>Total Progress: {totalProgress}%</h2>
{/* Render each mission with its progress and analytics */}
{data.missions.map((mission) => (
<div key={mission.id}>
<h3>{mission.name}</h3>
<p>Progress: {mission.progress}%</p>
{/* Additional analytics visualization */}
</div>
))}
</div>
);
```
4. **Enhancing the MissionModal Component**
- For the `MissionModal` component (`/src/components/modals/mission_modal.tsx`), you can
add UI elements to display and edit mission details, including progress and analytics. This might
involve conditional rendering based on whether the modal is in edit mode and utilizing the
`useQuery` and `useMutation` hooks to fetch and update mission data.
5. **Implementing Mission Iteration Toggle**
- Implementing mission iteration toggle points requires backend support for storing and
retrieving different mission versions. You'll need to extend your database schema and GraphQL
API to handle mission iterations. On the frontend, you could add a dropdown or slider in the
`MissionModal` to allow users to select the mission iteration they want to view.
**Conclusion**
This guide outlines the steps and provides examples for integrating "crew dashboard"
components into the Crew AI Visualizer. The actual implementation will depend on your specific
requirements, existing codebase structure, and design preferences.
—
**Overview**
The Swarmed Hierarchical Agents Reasoning Effectively & Responsibly (SHARER) initiative
leverages OpenAI's advancements in agent-based APIs to create a self-organizing and ethically
governed ecosystem of AI agents. Inspired by the Crewai and ACE Frameworks, SHARER
introduces a novel approach to AI governance and operation, where a hierarchy of specialized
agents collaborate to solve complex problems and perform a wide range of tasks.
**Theoretical Foundation**
SHARER is based on the idea that autonomous agents require a robust operational nexus to
make decisions aligned with human values and organizational goals. This is grounded in the
understanding that AI, like humans, cannot operate effectively without a set of guiding ideals.
SHARER addresses this by building a foundational nexus of multi-layered systems where each
tier of agents works within a defined functional scope guided by high ideals, ensuring decisions
are made with kindness, consideration to utility, and the provision of value.
**System Architecture**
**Board of Protocol Synthesis (BPS)**
At the top of the SHARER hierarchy is the Tier-1 Board of Protocol Synthesis (BPS), a virtual
"Stakeholder Leveraged Agent Procedural Suite" (SLAPS) of high-tier, high-level AI agents
modeled after wise, noble, courageous, effective leaders, and other powerful cultural
archetypes. The BPS's responsibilities include:
* Setting the bar and upholding the framework's tenants of kindness, monetization, income
acquisition, and profit generation for the greater good, as well as generosity of spirit with which
the SHARER seeks to help better the universe with, and seeing to the overarching mission of
the agent swarm.
* Helping to guide strategy for team processes and workflows for any given project.
* Making high-level decisions and judgments, including the creation and firing of other AI
agents.
* Overseeing the activities of all agents to ensure alignment with the system's core values and
objectives.
* Serving as a role-playing governance access management ecosystem rule-board
(RPGamerBoard) mechanism to maintain order and security within the system.
**Manager/Executive-Suite AI-Agents (MESA's)**
Below the BPS agents are the Tier 2 Manager/Executive-Suite AI-Agents (MESA's), thought of
similarly to the E-suite executive leadership structure within a corporation. The AI agents within
this particular tier are tasked with:
* Translating the BPS's directives into actionable plans and strategies.
* Overseeing specific operational domains like resource distribution, plan/process perfecting,
and objective execution and fulfillment.
* Communicating with one another in an effective manner to ensure that SHARER operations
go-off without a hitch.
**Worker Tier... Context-Aware Project Navigation-Agents (CAPN's)**
An army of Tier 3 Context-Aware Project navigation-Agents (CAPN's) are specialized agents
created by the BPS or MESA Agents to perform specific tasks. They are designed with
particular functions and knowledge bases to see-to the needs identified by the higher levels of
the AI agent hierarchy regarding the performative tasks that must be performed within a project.
They are responsible for the majority of the heavy lifting when it comes to fulfilling the more
granularly detailed aspects of project tasks. They can be thought of as the main Employee class
within a company. They are often trained on more detail specific and technical-based datasets
relevant to each individual agent's Role. These AI agents are tasked with things such as:
* Generating, organizing, testing, troubleshooting, and perfecting Code and performing other
coding related Tasks, such as building front-end and back-end architecture for different projects,
writing and deploying smart contracts on blockchains, et-cetera.
* Writing Blog posts and Social media posts.
* Posting to relevant platforms.
* Engaging in CRM related tasks such as the operation of Email Marketing Campaigns.
* Performing project research tasks.
* Compiling Documents
* Collating Data
* Creating and Maintaining Databases
**Agent Trained Assistant Tier Agents (AT-ATs)**
CAPN Agents may each Spawn up-to Five Tier-4 Agent Trained Assistant Tier agents (AT-ATs),
in order to help them complete more complex tasks more effectively and in a more timely
manner according to project requirements. These Agents are built to perform simple tasks such
as those which require a lot of repetitive actions. They are given their operational datasets
directly from the CAPN Agents' knowledge lockers, and trained by the agents specifically to
perform particular tasks the CAPN agent needs to overhaul. Tier-4 AT-AT agents can be thought
of similarly to assistants and maintenance crews within a company. For instances where
projects are up against deadlines, AT-AT agents have the ability to spawn only up to Two
Intern-Tier agents if a project calls for the need, to assist them in the same manner they are
assisting CAPN agents.
**Generalist Replicate Utility Normalization Technology Agents (INTERNS)**
All the way at the bottom of the totem pole at Tier-5, are the Generalist Replicate Utility
Normalization Technology Intern agents (INTERNS). They are only there as support staff when
absolutely needed due to workload of the higher Tiered agents. They do not have Spawning
privileges.
**Agent Configuration**
Each agent in the SHARER is defined by the following parameters:
* **Utility-Functions (a.k.a. Tools)**: AI agents are instantiated with a set of Tools,
(utility-functions) that enable them to perform and complete their assigned Tasks. These
utility-function tools include API interactions, internal process management, and the ability to
spawn additional assistant-agents if required, though this is limited by agent's only being
permitted to spawn additional agents who are tiered below them in the hierarchy. If additional
assistance is required by an agent possessive of the same authority as them in order to best
complete their assigned task so that it aligns with project directives, then they must make a
request to their immediate superiors for approval. The immediately superior agent will assess
the request, and if approval is given, they themselves will spawn the new requested agent for
their subordinate agent who made the request.
* **Backstories**: All AI agents have verbose backstories, massaging and buttering-up the LLM
behind each agent, detailing agent characteristics, tendencies, and relevant work histories
which make each perfectly tailored to their Role, and position within the team. These
backstories, (and similarly the Tasks and tools) must be vividly illustrated utilizing
highly-descriptive verbosity in order to guide the way they are supposed to perform their
assigned duties, thus descriptions must be precise and explicit in their composition. To further
increase each agent's effectiveness in their respective Roles, all AI agents will have access to a
personal locker, containing a selection of files that should serve as their extended knowledge
base, providing them further expertise and acumen in their particular field and Role, as-well-as
All of the information they need to carry out their assigned tasks effectively.
* **Instructions (Responsibilities + Tasks)**: Agents are given a set of instructions that outline
their [Roles & Responsibilities, Assigned Tasks & Tools, and their detailed, verbose backstories,
which include boasting descriptions of their work-histories, employable-methodologies,
motivating-goals, perfectly-clear definitions of tools at their disposal, the APIs they are
integrated with and have access to, along with any other operational directives.
* **Conversation Structure & Lines of Communication (LOCs)**: Interactions with agents are
structured in a conversational format, tailored to their role-based organizational structure, with
USER inputs (From a dedicated custom-built insight-dashboard React application ui, and
eventually an additional virtual avatar environment once the team scales) leading directly to
agent actions and responses.
* **Supervision**: Each and every AI agent works under the supervision of the BPS or
designated MESA Agents, ensuring adherence to the crew's overarching mission and ideals.
**Controlling Agents**
The Swarmed Hierarchical Agents Reasoning Effectively & Responsibly (SHARER) operate on
a guidance mechanism that governs the instantiation, management, and termination of agents
within the crew. This guidance mechanism is designed to maintain order, security, and alignment
with the overarching goals and ideals of the SHARER.
**Instantiation and Termination**
All AI agents within the SHARER are endowed with the capability to instantiate and terminate
other AI agents, but these abilities are bound by the clearly defined hierarchical and role-based
rules:
* **Instantiation**: Every AI agent (Except for those in Tier 5) has the functional ability to create
new agents. However, an agent can only instantiate agents who are on a lower-level tier of the
hierarchical structure below the one that they are on. This ensures that the creation, or ‘hiring’ of
new agents is a deliberate, purposeful, and controlled process, maintaining the integrity of the
hierarchy's structure.
* **Termination**: Agents possess the ability to terminate or "Fire" other AI agents within their
roles lineage. Any AI agent can terminate any other descendant AI agent that it has created
directly or indirectly. This allows for the removal of agents that are no longer needed, have
completed their tasks, or are not performing as intended in order to keep the crew from getting
too cumbersome to run smoothly.
**Tiers, Levels, Roles, and Privileges**
When an agent is created, it is assigned a specific LEVEL, and set of ROLES,
RESPONSIBILITIES and TIER-PRIVILEGES that define the scope of it’s defined TASKS:
* **Level**: The level of an AI agent determines its position within the hierarchy and is indicative
of its authoritative influence. Higher-level agents have broader strategic roles, while lower-level
agents have more specialized, granularly detailed, and task-oriented responsibilities. The
general structure of agent levels is as follows;
* – Level 1: All Tier-5 agents and some Tier-4 agents
* – Level 2: All Tier 4
—-
**Project Management Documents**
**Project Charter**
* Defines the project scope, objectives, deliverables, and stakeholders.
* Outlines the project's timeline, budget, and quality standards.
* Provides a clear understanding of the project's purpose and expectations.
**Project Plan**
* Details the project's activities, dependencies, and timelines.
* Identifies the resources required and assigns responsibilities.
* Establishes a framework for project execution and monitoring.
**Risk Management Plan**
* Identifies potential risks to the project and their likelihood and impact.
* Develops strategies to mitigate or avoid risks.
* Ensures that the project team is prepared to handle unexpected events.
**Communication Plan**
* Outlines the communication channels, frequency, and responsibilities.
* Establishes protocols for stakeholder engagement and information dissemination.
* Facilitates effective communication throughout the project lifecycle.
**Quality Management Plan**
* Defines the quality standards for the project's deliverables.
* Establishes processes for quality control and assurance.
* Ensures that the project meets the required quality levels.
**Systems Documents**
**System Requirements Specification (SRS)**
* Defines the functional and non-functional requirements of the system being developed.
* Provides a detailed description of the system's behavior and capabilities.
* Serves as a basis for system design and testing.
**System Design Document (SDD)**
* Describes the architectural design of the system, including its components, interfaces, and
data structures.
* Outlines the system's implementation details and technical specifications.
* Provides a blueprint for system development.
**User Manual**
* Provides instructions on how to use the system effectively.
* Explains the system's features, functionality, and limitations.
* Supports end-user training and adoption.
**Test Plan**
* Outlines the testing strategy, test cases, and expected results.
* Defines the criteria for system acceptance and validation.
* Ensures that the system meets its functional and performance requirements.
**Maintenance Manual**
* Provides instructions on how to maintain and update the system.
* Includes troubleshooting procedures, upgrade instructions, and support contact information.
* Facilitates ongoing system maintenance and support.