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Evaluation Systems for AI Code Agents

7 chapters · 30 lessons

1. 1. Anatomy of Repository Coding Agents

   4 lessons

   Understand how coding agents interact with repositories, tools, and development environments to execute multi-step tasks.

2. 2. Building Controlled Evaluation Environments

   4 lessons

   Set up isolated, reproducible repository states where agents can execute tasks without production risk.

3. 3. Multi-Signal Grading Systems

   5 lessons

   Design graders that combine functional tests, static analysis, and custom checks to evaluate agent output quality.

4. 4. Task Stratification and Risk Frameworks

   4 lessons

   Create policies that assign different trust levels and review requirements based on change type and repository area.

5. 5. Eval Suite Construction from Real Work

   4 lessons

   Build evaluation datasets from historical issues, pull requests, and production incidents to test agent capabilities.

6. 6. Integration with Development Pipelines

   4 lessons

   Embed agent evaluation into CI/CD workflows, pull request automation, and continuous delivery systems.

7. 7. Governance, Auditability, and Regression Tracking

   5 lessons

   Establish systems for logging agent decisions, tracking performance over time, and maintaining compliance requirements.