agent-harness-kit

# agent-harness-kit

**The infrastructure layer that makes AI agents production-ready.**

Solo-dev harness engineering kit for Claude Code, with an experimental Codex-readable runtime surface. One command, ~30 minutes, and your hobby project gets the patterns that took OpenAI from prototype to 1M lines of agent-generated code: layered architecture, structural tests, garbage collection, review subagents, JSON feature tracking, and pre-completion checklists — without the enterprise overhead.

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## The Harness Engineering Shift

**February 2026**: OpenAI published "[Harness engineering: leveraging Codex in an agent-first world](https://openai.com/index/harness-engineering/)" documenting how their Frontier Product Exploration team built an internal product with **~1 million lines of code over 5 months** — with zero lines manually written by humans.

**The results:**
- 3 engineers → 7 engineers
- **~1,500 PRs merged** (3.5 PRs per engineer per day)
- Each engineer operating at **3-10x capacity** through agent delegation
- Agents running autonomously for **6+ hours per task**
- **~1 billion tokens processed per day**

**The insight:** The work shifted from *writing code* to *engineering the harness* — the infrastructure, constraints, and feedback loops that make agents reliable at scale.

**March 2026**: LangChain demonstrated this principle empirically. By improving their agent harness alone (no model changes), they jumped from **52.8% → 66.5%** on Terminal-Bench 2.0, [climbing 25 spots on the leaderboard](https://www.langchain.com/blog/improving-deep-agents-with-harness-engineering).

**The pattern is clear:** Harness quality matters more than model choice for production outcomes.

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## Why This Kit Exists

You're a solo developer or small team. You don't have OpenAI's infrastructure budget or Stripe's agent platform team. But you *can* adopt the same patterns at hobby-project scale:

### What you get:
- **Proven patterns from production harnesses** — OpenAI's two-fold initializer/coding-agent split, Anthropic's CLAUDE.md table-of-contents approach, Mitchell Hashimoto's ["engineer the harness"](https://mitchellh.com/writing/my-ai-adoption-journey) discipline
- **33 skills** that codify rituals from teams shipping agent-generated code at scale (`/add-feature`, `/context-query`, `/garbage-collection`, `/remember-project`, `/project-status`, `/review-this-pr`, etc.)
- **10 read-only review subagents** for cheap second-opinion passes and mandatory done-claim advice (advisor, architecture, security, reliability, performance, API consistency, trace failure, eval rubric, adapter compatibility, release readiness)
- **Structural enforcement** via TypeScript, Python, Go, Rust, Swift, and Kotlin adapters — catch layer violations and high-risk shortcuts before they compound
- **Architecture fitness plugins** — repo-local JSON rules for env, DB, provider, validation, and public-API boundaries with reviewer routing
- **Policy packs** — stack-specific governance defaults for `nextjs-saas`, `api-backend`, and `python-data`
- **Cost guardrails and attribution** — default budget plus provider-call cost by skill, task, and cache read/write bucket
- **Model routing evidence** — lane-level model usage report so cheap explore lanes and stronger implementation/review lanes are measured, not guessed
- **Sanitized trace corpus** — public success/failure traces for tiny, normal, high-risk, false-done, overbroad-edit, reviewer-gap, replay, bypass, and runtime-parity cases
- **JSON feature tracking** (not Markdown) — Anthropic's pattern for machine-readable planning
- **Task contracts + evidence bundles** — a feature can only move to `passes: true` when the current diff has machine-readable proof, concrete checks, and a diff summary
- **SQLite operational state** — local `harness.db` records intake, stories, decisions, backlog, traces, friction, and trace quality without hand-editing Markdown tables
- **Context rules + trace scoring** — phase-by-lane retrieval guidance plus minimal/standard/detailed trace quality gates for tiny, normal, and high-risk work
- **Orchestration contracts** — multi-agent runs bind lanes, tool policy, required reviewers, task ids, and output artifacts to a checked workflow contract
- **Failure-to-rule records** — every recurring agent miss can be captured as JSON and promoted into a durable harness prevention
- **Adversarial eval suite** — deterministic red-team probes for fake evidence, missing high-risk attestation, protected-path bypasses, unsafe eval commands, unreviewed bypasses, and prompt-level hook bypass attempts
- **Pre-completion checklists** — OpenAI's golden-principles garbage collection ritual, scaled to top-3 fixes per week

### What this kit does NOT claim:
- **Struct

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