Awesome Harness Engineering

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.

Contents

• 📐 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
• 🎓 Tutorials & Educational
• 🏭 Generators & Meta-Harnesses
• 🧪 Demo Harnesses
• 🗂️ Adjacent Collections
• 🔒 Security, Sandbox & Permissions
• ✅ Evals & Verification
• 📋 Templates
• 🏭 Production Infrastructure & Operations
• 📚 Related Awesome Lists
• 🤝 Contributing

Foundations

Canonical essays that define what harness engineering is and why it matters.

• My AI Adoption Journey — The essay that coined "harness engineering" (February 2026): Mitchell Hashimoto's six-stage adoption path culminates in engineering the harness itself — every agent mistake becomes a permanent fix, whether an AGENTS.md line or a purpose-built tool, so the error class never recurs. The Ghostty numbers (commits needing correction dropping from 40% to 3% over three months of accumulated harness fixes) are the founding empirical argument that the harness, not the prompt, is the reliability surface.
• 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.
• Building Effective Agents — Anthropic's foundational guide on agent architecture, covering when to use workflows vs. agents and how to compose primitives.
• Harness Design for Long-Running Application Development — Anthropic's engineering blog on designing harnesses for sustained, multi-session development tasks. Key insight: every harness component assumes the model can't do something; those assumptions expire.
• Writing Effective Tools for Agents — Anthropic's guide on tool interface design: naming, schemas, error surfaces, and the principle that tool design is agent UX.
• Beyond Permission Prompts — Anthropic on building structured permission and authorization systems into agent harnesses instead of relying on natural-language permission text.
• Demystifying Evals for AI Agents — Anthropic's framework for evaluating agent behavior: what to measure, how to build eval harnesses, and why unit-test-style evals fail for agents.
• What is an AI Agent? — IBM's definitional piece, useful for anchoring harness design decisions to a clear model of what an agent actually is.
• Agent Development Kit: Making it easy to build multi-agent applications — Google's announcement and design rationale for ADK: explains the multi-agent topology, tool registration model, and eval pipeline that shaped their framework. Complements the Anthropic/OpenAI framing with Google's production perspective.
• Harness Engineering — Martin Fowler's synthesis of what harness engineering practice looks like: three interlocking systems — context engineering (curating what the agent knows), architectural constraints (deterministic linters and structural tests), and entropy management (periodic agents that repair documentation drift). The "humans on the loop" framing — harness engineers who design and maintain agent environments rather than inspecting individual outputs — is the clearest conceptual map of what the discipline actually entails.
• The Anatomy of an Agent Harness — LangChain's structural breakdown of the five primitives that compose a harness: filesystem (durable state + agent collaboration surface), code execution (autonomous problem-solving without pre-designed solutions), sandbox (isolation + verification), memory (cross-session persistence), and context management (compaction against "context rot"). The co-evolution warning — models trained with specific harnesses can become overfitted to those designs — explains why harness architecture choices have lasting consequences beyond the immediate task.
• Building AI Coding Agents for the Terminal: Scaffolding, Harness, Context Engineering, and Lessons Learned — The first systematic practitioner paper on terminal-native coding agent harness design: eager-construction scaffolding (pre-build all components before the first message to eliminate first-call latency and race conditions), compound multi-model architecture (different model instances for execution, reasoning, critique, and vision tasks), 5-layer defense-in-depth safety, and schema-filtered planning subagents (enforce behavioral constraints via tool schema rather than runtime permission checks). The five lessons distilled from building OpenDev apply to any server-side agent harness.
• Natural-Language Agent Harnesses — Proposes externalizing agent control logic as portable natural-language artifacts (NLAHs) executed by a shared Intelligent Harness Runtime, enabling harness design to be studied, transferred, and reproduced rather than buried in bespoke controller code. Directly addresses the root cause of harness fragility: control logic scattered across framework defaults and hard-coded controller logic that can't be inspected, versioned, or transferred.
• Ranking Engineer Agent (REA): Meta's Autonomous AI System for Ads Ranking — Meta's production harness for multi-day ML pipeline automation with hibernate-and-wake checkpointing for resuming interrupted 6-hour tasks without losing context. Demonstrates harness design for scientific workflows where individual turns can exceed model context limits but the overall pipeline must maintain coherence across days.
• Supercharge Your AI Agents: The New ADK Integrations Ecosystem — Google's 2026 update to Agent Development Kit expanding the ecosystem integrations (Hugging Face, GitHub, Daytona, Notion, etc.) and providing reference patterns for how orchestration harnesses wire external services without losing determinism or state coherence.
• 2026 Agentic Coding Trends Report — Anthropic's industry benchmark identifying infrastructure configuration as a first-class optimi