How Docker Compose Can Create Reproducible AI Agent Evaluation Environments

Inconsistent AI agent evaluation results between local and CI environments are often caused by differences in tool responses, database state, clock settings, and dependency versions rather than the model itself. A Docker Compose-based evaluation lab can eliminate much of this accidental variability by providing a versioned, controlled environment for replaying synthetic test cases. The setup uses Compose profiles to separate core services from optional components like fault injectors, telemetry collectors, and local model runners, keeping baseline runs lightweight. Container images are pinned by digest, dependencies locked, and a JSON manifest records all attempted conditions — including seed, clock, prompt, and policy revisions — for each evaluation run. Structured evidence artifacts are written to a mounted directory after each run, making behavioral regressions easier to identify and distinguish from infrastructure-level changes.