README
Awesome Agent Harness 
A curated list of tools, frameworks, and resources for agent harness engineering — the discipline of designing environments, constraints, and feedback loops that make AI coding agents reliable at scale.
What is an Agent Harness?
An agent harness is the infrastructure that wraps around an LLM coding agent. It's everything except the model itself: session management, context delivery, tool design, architectural enforcement, failure recovery, and human oversight.
OpenAI's Harness Engineering blog defined the term: "When a software engineering team's primary job is no longer to write code, but to design environments, specify intent, and build feedback loops that allow agents to do reliable work." Their team built 1M+ lines of production code with zero human-written lines using this approach.
Anthropic's Claude Code team discovered the same principles from the tool design side: the harness matters more than the model. Fewer, more expressive tools beat a long menu of narrow ones. Progressive disclosure — letting the agent recursively discover context across layers — outperforms loading everything upfront. "Designing an agent's action space is as much an art as it is a science."
Core Principles
From the two seminal references above:
- Humans steer, agents execute — Engineers design environments and review outcomes, not write code
- Repository knowledge is the system of record — If it's not in the repo, it doesn't exist to the agent. Slack threads, Google Docs, and tribal knowledge are invisible
- AGENTS.md is a table of contents, not an encyclopedia — Point to deeper sources of truth; don't dump everything in one file
- Enforce architecture mechanically — Custom linters, structural tests, and CI checks replace code review for invariants
- Agent legibility is the goal — Optimize code for agent readability first, human readability second
- Fewer tools, more expressiveness — Progressive disclosure and composable primitives beat sprawling toolkits
- See like an agent — Read the model's outputs, watch where it struggles, and evolve the harness accordingly
- Corrections are cheap, waiting is expensive — At high agent throughput, fix-forward beats blocking merge gates