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Build a reproducible AI agent evaluation lab by making each run a controlled experiment: keep the task, fixtures, agent and model configuration, container environment, scoring rules, and output artifacts explicit. Docker Compose can describe the lab’s services, networks, mounts, and environment settings, but it does not by itself make results reproducible. The evaluation runner still needs to isolate work, record what happened, score the behavior, and compare repeat runs against a baseline.
What a reproducible evaluation needs
A score is only useful when you can explain what produced it and run the same conditions again. Treat every evaluation as a named experiment with a reviewable task definition and enough saved configuration to identify the exact setup.
- Task: the input the agent receives and the expected behavior, such as expected tool calls or properties of the final response.
- Environment: the container image, working directory, setup steps, mounted fixtures, available tools, resource limits, and relevant environment configuration.
- Agent configuration: the agent and model identifiers, prompts, tool definitions, and dependency versions used for the run.
- Scoring: explicit checks for the task, with model-judged criteria kept distinct from deterministic checks.
- Evidence: the run report, logs, session or event data, and task outputs needed to inspect a result.
- Comparison: repeat runs and a saved baseline so variation and regressions are visible.
Store these details alongside each run. A result without its task and configuration is difficult to reproduce or interpret.
Choose the lab boundaries before writing Compose
Use Compose to make the lab’s services and boundaries legible, not as a claim that there is one universally correct agent-evaluation stack. The available documentation describes useful patterns, but it does not establish a complete, pinned Compose reference implementation for this exact lab.
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A practical design separates these responsibilities, whether they become separate services or remain parts of one runner:
- Task definitions and fixtures: reviewable cases plus any setup files or working directories they need.
- Agent runner: the process that invokes the selected agent in a container-compatible environment.
- Scoring: deterministic checks and, where appropriate, a separately identified LLM judge.
- Artifacts: persistent storage for reports, logs, session data, and task outputs.
Make the network access, mounts, environment variables, and writable paths explicit in the Compose project. Decide which directories are read-only, which state is task-local, and which output directory survives container teardown. Validate those choices with the agent and workload you actually use; neither Compose nor the cited evaluation examples establish a universal configuration.
Define cases so another run can inspect them
Keep each task in a reviewable file or similarly versioned format. Include the user input, expected tool behavior or response properties, fixture and setup details, working directory, and scoring criteria. Docker Agent’s evaluation documentation uses sessions that capture a user question and expected tool calls, with optional response criteria; its session and working-directory fields illustrate a useful case shape. The exact schema is specific to that runner, not a general Compose standard.
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Prefer checks that say what observable behavior counts as success. For a tool-using task, specify the expected action sequence or acceptable calls separately from the quality criteria for the final answer. Keep test data stable, and record any state that setup scripts create so a rerun starts from the intended conditions.
Isolate execution and control task state
Isolation is part of the measurement: an agent can behave differently if it inherits files, caches, environment variables, or resources from a previous task. Docker Agent describes evaluations running inside containers and supports setup scripts. Workspace-Bench describes a stricter pattern: a fresh container per task, task-local HOME, temporary and cache directories, and a read-only repository mount. These are documented examples, not requirements for every evaluation.
For a benchmark that needs consistent machine limits, Workspace-Bench’s documented default profile is 2 CPUs, 8 GiB of memory, 512 PIDs, and 20 GiB of writable task storage per task. Those figures describe Workspace-Bench’s protocol; choose and record limits appropriate to your own workload rather than copying them as universal recommendations.
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Record the isolation policy with the run: whether each case gets a fresh container, what is mounted, what can be written, which tools and credentials are available, and what resource limits apply. If cases share a service or stateful dependency, define how it is reset between runs.
Score actions and answers separately
A final answer alone may hide a failed tool call or an unintended action. Match the scoring method to the task and preserve the component results instead of collapsing everything into one opaque pass/fail value.
| Evaluation dimension | What it helps reveal | Documented example |
|---|---|---|
| Tool-call behavior | Whether the agent selected and executed the expected tools or actions. | Docker Agent documents tool-call F1. |
| Response relevance | Whether the answer addresses the task’s stated criteria. | Docker Agent documents an LLM judge for relevance statements. |
| Output size | Whether the response falls into the expected size category. | Docker Agent documents an output-size category. |
| Cost | How much a run cost, when the runner reports it. | Docker Agent reports cost, but its documentation says cost is not used by its regression gate. |
Tool-call F1 and an LLM relevance judgment answer different questions. Preserve both when they apply, and label judge results as judgments rather than deterministic facts. Docker Agent’s documentation notes that an LLM judge can vary, so a deliberate tolerance may help avoid noisy aggregate gates. Its documented behavior still gates a transition from pass to fail.
Repeat runs and compare against a baseline
Run cases more than once when you need to understand run-to-run variation. Save the initial result as a named baseline, then compare later runs using the same task suite and environment. Docker Agent supports repeat counts and comparison with a saved prior run. Keep model, agent, prompt, dependency, task, and image identifiers with each result; the cited documentation does not define a complete manifest schema for this broader Compose lab.
- Freeze the evaluation inputs. Version the case definitions and fixtures, and identify the agent, model, prompt, dependencies, and container image.
- Run under the recorded environment. Apply the same isolation, mounts, tool access, credentials, and resource profile for each configuration being compared.
- Repeat selected cases. Use repeats to observe variation rather than treating a single run as conclusive.
- Save the baseline and artifacts. Retain the report and the logs or session data needed to explain its component scores.
- Set regression rules deliberately. If a judge can vary, choose a tolerance with that uncertainty in mind; do not silently treat cost or another reported measure as a gate unless your chosen runner does so.
When comparing models or agent configurations, hold the task suite and environment constant. Report task outcomes, tool-call behavior, response quality, repeat variation, resource profile, and cost when available. A comparison is not fair if one configuration receives different tools, credentials, fixtures, or limits.
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Credential forwarding is runner-specific. Docker Agent’s evaluation guide says provider credentials are forwarded automatically in its workflow, but GITHUB_TOKEN and GH_TOKEN are not forwarded automatically; its documented GitHub Copilot setup requires explicit handling in the CLI. The guide also distinguishes its LLM judge, which runs on the host. These behaviors describe Docker Agent, not every Compose project.
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For your own lab, grant only the access a task requires, avoid putting secrets in versioned case files or logs, and document whether the runner or a host-side judge receives credentials. Confirm the behavior of the selected runner before relying on Compose environment settings to pass secrets through.
Keep benchmark claims tied to their setup
A benchmark score describes the tested benchmark and configuration, not agent quality for every task. OpenAI reports a 21.0% average replication score for Claude 3.5 Sonnet (New) with open-source scaffolding as the best-performing tested agent in its PaperBench evaluation. That figure belongs to that specific benchmark and setup; it is not a general expected score for a Docker Compose lab or unrelated agent tasks.
What the cited examples do—and do not—establish
Docker’s evaluation documentation describes containerized evaluation, setup and working-directory support, repeats, baseline comparison, and metrics such as tool-call accuracy, relevance, and output size. Workspace-Bench supplies a separate example of fresh per-task containers and a fixed resource profile. Together, these patterns help shape a lab, but they do not specify a universal Compose file, image-pinning convention, dependency-locking recipe, or agent-evaluation standard. Docker Agent’s CLI flags, defaults, and credential behavior can change; verify them against its current documentation when implementing your setup.
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