Automate AI Evals with Claude Code

Design multi-step workflows that execute your full evaluation pipeline without manual intervention

Structure agent prompts, tool access, and orchestration for reliable eval execution

Handle failure modes gracefully so workflows recover rather than break

Build scoring systems that assess evaluation results for statistical soundness, accuracy, and feasibility

Define rubrics your agents can apply consistently across hundreds of eval runs.

Calibrate automated scores against human judgment to establish trust boundaries.

Build systems that evaluate results, identify deficiencies, generate improvements, and re-evaluate automatically

Set convergence criteria so the system knows when to stop iterating

Monitor loop behavior to detect degradation or drift over successive iterations

Run dozens of evaluation experiments in parallel and cross-reference results for validity

Build experiment registries that track configurations, outcomes, and comparisons

Surface which approaches produce meaningful improvements versus noise

Connect automated eval systems to real product data, not just test fixtures

Define escalation paths for when automated evaluation surfaces ambiguous or high-stakes findings

Translate automated eval output into ship, iterate, pause, and rollback recommendations