Skill Issue: Harness Engineering for Coding Agents — HumanLayer
FreeSub-agents as context firewalls, practical patterns
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About Skill Issue: Harness Engineering for Coding Agents — HumanLayer
Discusses the concept of harness engineering for coding agents, emphasizing that many failures are configuration problems rather than model limitations. Introduces sub-agents as context firewalls and practical patterns using skills, MCP servers, hooks, and back-pressure mechanisms to improve agent reliability and output quality. Based on real-world experience with dozens of projects and hundreds of agent sessions.
Key Features
Sub-agents as context firewalls to prevent context contamination
Harness configuration levers: system prompt, tools/MCPs, context, sub-agents, hooks, skills
Skills for progressive disclosure of knowledge
Hooks for automated integration and deterministic control flow
Back-pressure mechanisms to manage context window inflation
Focus on configuration to get the most out of today's models
Pros & Cons
Pros
- Provides practical, actionable patterns that work with current models
- Shifts focus from waiting for better models to improving configuration
- Sub-agents act as context firewalls, isolating tasks and preserving focus
- Hooks and skills enable progressive disclosure of knowledge and deterministic control
Best For
Improving reliability of coding agents on moderately hard tasksTeaching agents about codebase specifics not in training dataAdding determinism beyond system prompt instructionsPreventing context window from inflating with bad contextIncreasing task success rates without relying on magic prompts
FAQ
What is harness engineering?
Harness engineering is the practice of leveraging configuration points (system prompt, tools/MCPs, context, sub-agents, hooks, skills) to customize and improve coding agent output quality and reliability.
How does harness engineering relate to context engineering?
Harness engineering is a subset of context engineering, which is a superset of prompt engineering and techniques for systematically improving AI agent reliability.