Evaluation of AI Agents

Evaluate agent behavior over time, not just answers. A comprehensive framework for building production-ready evaluation systems.

Correctness, trajectory, safety, robustness, efficiency, UX

Deterministic tests, model-based rubrics, human judgment

CI gates, nightly runs, release candidate validation

Why This Framework?

Traditional eval systems focus on single-turn correctness. This framework addresses the unique challenges of evaluating agentic systems:

•Long-horizon behavior: Agents make multiple decisions over time, not just one answer
•Tool interactions: Evaluate how agents use tools, not just final outputs
•Safety & robustness: Test adversarial scenarios and edge cases
•Production incidents: Replay failures as regression tests

Complete CTO/Tech-Lead guide with 20 sections covering evaluation strategy, metrics, grader types, and implementation roadmap.

YAML-based task suite structure with schemas, configuration, and suite breakdown for golden, open-ended, adversarial, and failure-replay tests.

Three standardized 0-4 scoring rubrics for evaluating trajectory quality, safety policy adherence, and output quality.

Four categorized evaluation tasks demonstrating golden tests, open-ended evals, adversarial testing, and failure replay patterns.