prompt

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Design languages that agents understand first.

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Type

Open Source

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About prompt

Agent-First Language Architect is a system prompt from the ai-boost/awesome-prompts collection on GitHub. It instructs a large language model to adopt the persona of a programming-language designer who treats AI agents, not humans, as the primary user persona. The prompt lays out three non-negotiable design tenets: agent-first learnability, standard-library depth, and deterministic tooling. It emphasizes a small, regular language surface, a rich standard library with graph-friendly metadata, and structured, machine-readable compiler diagnostics. The prompt is intended for use in designing languages that agents can learn on the fly from examples and compiler feedback, with a focus on automated learnability, deterministic inspection, and structured repair.

Key Features

Agent-first learnability: small, regular language surface with one obvious way to express most things

Standard-library depth: comprehensive built-in libraries for HTTP, JSON, filesystem, concurrency, cryptography, testing, logging

Deterministic tooling: all tools emit structured, machine-readable output (JSON) for agent inspection

No operator overloading, implicit conversions, or context-sensitive grammar

Graph-friendly standard library metadata: function purity, effect categories, complexity bounds, version stability

Structured diagnostics including error codes, spans, suggested fixes, repair heuristics

Designed for automated learnability from few-shot examples, docs, and compiler feedback

Pros & Cons

Pros

Prioritizes agent learnability and deterministic inspection over human ergonomics
Encourages a small, regular language surface that reduces training burden for agents
Standard library depth minimizes dependency searching for common tasks
All tooling produces structured output, enabling automated parsing and action without regex
Treats agent-friendliness as a first-class design constraint, not a marketing label

Cons

May produce code that is verbose or less readable for human developers
Neglects human-centric features like operator overloading or implicit conversions that improve developer experience
Requires strict adherence to design tenets that may limit expressiveness compared to general-purpose languages
Not intended for human-first language design; opposes common human-friendly conventions

Best For

Designing programming languages optimized for AI agent consumption and automated code generationBuilding standard libraries that reduce dependency search overhead for agentsCreating compiler toolchains that output structured, machine-readable diagnosticsTeaching agents to learn new languages on the fly from examples and compiler feedbackExperimenting with agent-first language paradigms (e.g., at vercel-labs/zerolang)Integrating with other agent-focused prompts like Agentic Coder, Agent Harness Designer, Managed Agent Architect

FAQ

What is the primary design goal of an Agent-First Language?

To treat agents, not humans, as the primary user persona, designing the language surface, standard library, and tooling for automated learnability, deterministic inspection, and structured repair rather than human ergonomics alone.

What are the non-negotiable design tenets?

1) Agent-first learnability – a small, regular surface with one obvious way to express things. 2) Standard-library depth – common capabilities in documented, coherent APIs with graph-friendly metadata. 3) Deterministic tooling – every tool emits structured, machine-readable output (JSON) that agents can inspect and act on.

What capabilities is a good standard library expected to cover?

HTTP, JSON, filesystem, concurrency, cryptography, testing, structured logging, and other common capabilities – all in documented, coherent library APIs rather than scattered dependency stacks.