Curated resources, patterns, and templates for building reliable AI agent harnesses.
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Harness engineering is the discipline of designing the scaffolding — context delivery, tool interfaces, planning artifacts, verification loops, memory systems, and sandboxes — that surrounds an AI agent and determines whether it succeeds or fails on real tasks.
This list focuses on the harness, not the model. Every component here exists because the model can't do it alone — and the best harnesses are designed knowing those components will become unnecessary as models improve.
- 📐 Foundations
- 🧩 Design Primitives
- 🔄 Agent Loop
- 🗺️ Planning & Task Decomposition
- 📦 Context Delivery & Compaction
- 🔧 Tool Design
- 🔌 Skills & MCP
- 🛡️ Permissions & Authorization
- 🧠 Memory & State
- ⚙️ Task Runners & Orchestration
- ✔️ Verification & CI Integration
- 👁️ Observability & Tracing
- 🐛 Debugging & Developer Experience
- 🧑💼 Human-in-the-Loop
- 🔍 Reference Implementations
- 🔒 Security, Sandbox & Permissions
- ✅ Evals & Verification
- 📋 Templates
- 🏭 Production Infrastructure & Operations
- 📚 Related Awesome Lists
- 🤝 Contributing
Canonical essays that define what harness engineering is and why it matters.
- Harness Engineering — OpenAI's framing of harness engineering as a discipline: how to design the scaffolding that lets Codex and similar agents operate reliably in an agent-first world.
- Unrolling the Codex Agent Loop — OpenAI's detailed breakdown of the Codex agent loop, exposing each harness component and where it can be improved.
- Run Long-Horizon Tasks with Codex — OpenAI's practice guide for long-horizon task planning: introduces Plan.md, Implement.md, Documentation.md as reusable harness artifacts.