Build a Reproducible AI Agent Evaluation Lab with Docker Compose
An agent evaluation fails in CI but passes locally. Before blaming the model, ask whether both runs saw the same tool responses, database state, clock, configuration, and dependency versions. Containers cannot make an external model deterministic. They can remove a large amount of accidental variability around it. I use Docker Compose as an evaluation lab: a small, versioned environment that can…
Docker Compose can be used to create a reproducible AI agent evaluation lab. By providing a controlled environment, it can help remove accidental variability around external models. Docker Compose services can be profiled to enable specific modes, such as fault injection or observability. A basic lab can be run with the command `docker compose run --rm eval-runner`.
Optional services, such as fault injectors or telemetry services, can be added when testing specific aspects of the system. To ensure reproducibility, an environment manifest should be recorded, which includes details such as the case ID, dataset revision, prompt revision, policy revision, tool schema revision, model ID, temperature, seed, and clock.
Tools should be pinned where possible, including container images by digest, JavaScript dependencies with a lockfile, synthetic datasets by content hash, prompt, policy, and tool-schema revisions, locale, timezone, clock, and random seed, provider and model identifiers. A bounded artifact set, such as environment.json, results.json, junit.xml, traces/, and README.md, should be persisted outside the containers.
This allows for a more detailed analysis of the evaluation results and makes it easier to identify any regressions or improvements. CI can upload the evidence directory even when the runner fails, ensuring that the evaluation results are not lost. A machine-readable comparison file can be created to compare the candidate model with a baseline model, providing a more comprehensive view of the evaluation results.
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