Agent Readiness Framework

A few weeks ago I wrote that adopting coding agents requires strong engineering practices.
Test stability, linting, documentation, security controls matter much more than a particular harness or model.

Agent Readiness framework is an attempt to formalize these criteria for a particular repository and define how much autonomy can be safely delegated to agents.

The framework evaluates repos across 8 dimensions:
- Style & Validation
- Build System
- Testing
- Documentation
- Dev Environment
- Code Quality
- Observability
- Security & Governance

Based on these dimensions, the framework defines 5 levels of repo maturity:
🔸 Level 1: Functional. Basic checks: README, linters, unit tests.
🔸 Level 2: Documented. Detailed documentation and basic automations: AGENT.md, reproducible dev env, contribution guides.
🔸 Level 3: Standardized. E2E tests, observability, security scanning, maintained documentation.
🔸 Level 4: Optimized. Fast validation loops, canary deployments, build optimization. Process is optimized for fast feedback.
🔸 Level 5: Autonomous. Task decomposition, multi-service orchestration, self-healing logic, auto-remediation.

The idea is simple: the higher the maturity level, the more predictable and reliable agent results. But looking at these levels, I can see that most repos are actually somewhere between Level 1 and Level 3.

Framework authors also provide a tool to automatically measure these criteria and maturity level, but it's available only after registration and using proprietary APIs. Scanned examples you can find at https://factory.ai/agent-readiness.

There is also an open-source alternative https://github.com/kodustech/agent-readiness. The project doesn't look active, but it gets the job done. It analyzes the repo and generates a report with the overall maturity level, findings for each dimension, and suggestions for improvements. Some rules are not very accurate. Looks like the project was mainly designed for python and js code verification. But anyway the tool gives you a good sense of what to pay attention to in your codebase.

What I like about this framework is that it shows that agent effectiveness is actually limited by the maturity of engineering practices. And it provides measurable and actionable results, that are easy to convert into an improvement plan for a particular repo.

#ai #engineering