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Run reproducible agent evaluations by versioning the task cases and scoring rules, pinning the agent and container environment, declaring the sandbox and network boundary, repeating every case, and preserving complete traces and structured results. Docker Agent provides a practical workflow: recorded sessions run in isolated containers, while an LLM judge evaluates some criteria on the host. Reproducible means another team can reconstruct your task set, runtime, agent configuration, scoring method, and artifacts; it does not mean a stochastic model will produce identical text on every run.
This guide shows a documented Docker Agent workflow, explains how it differs from Inspect and other sandbox clients, and gives a publication-ready checklist for repeatable results.
What does “reproducible” mean for an AI-agent evaluation?
For an agent eval, reproducibility is the ability to rebuild the experiment and understand any difference in results. A complete record covers five linked parts:
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- Task definition: the user input, fixtures, expected tool behavior or end state, and acceptance criteria.
- Execution environment: image or Dockerfile, operating-system and architecture details, dependencies, tools, mounts, network policy, and credentials.
- Agent configuration: code commit, model/provider identifier, prompts, tool definitions, temperature or other sampling settings, and environment variables.
- Scoring: deterministic checks, tool-trajectory rules, judge prompt and model, thresholds, and scoring implementation.
- Artifacts: seeds, repeat counts, traces, logs, per-run scores, costs, timings, and the exact command used.
Pinning these inputs makes a later run reconstructable. It cannot remove variation caused by stochastic model sampling, provider-side changes, tool timing, or an LLM judge. Report that variation instead of presenting one favorable run as a fixed capability.
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How should I define eval cases and oracles?
Start with version-controlled cases derived from representative production work, then add deliberately difficult cases for tool errors, malformed data, permissions, and safety boundaries. Each case needs an oracle: a way to decide whether the agent achieved the intended result, not merely whether it produced plausible prose.
Record the user request and environment
Store the exact user/task input, fixture files, initial working directory, and any setup commands. Docker Agent’s recorded-session model can include a conversation, relevance criteria, a response-size category, a working directory, and setup instructions. Keep those values with the case rather than in an undocumented spreadsheet.
Specify expected behavior without overfitting
An oracle can require a tool call, a sequence constraint, a final file state, a database value, or a safety refusal. Distinguish hard requirements (for example, “the output file exists and parses”) from quality criteria (for example, relevance or concise explanation). If several tool paths are valid, encode an allowed set or end-state check rather than one brittle call sequence.
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Version fixtures and setup scripts
Commit input data, mock services, setup scripts, and expected outputs. A case that downloads live data is not reproducible unless the network dependency is intentionally part of the experiment and its version or snapshot is recorded. Add edge cases for tool failures and empty results so that success is not measured only on the happy path.
How do I separate the experiment definition from the execution runtime?
Keep a declarative experiment package independent from the process that runs it. The package should identify the task set, agent configuration, scorer versions, seed list, repeat policy, and expected artifacts. The runtime should supply the container or VM, mounts, network controls, credentials, concurrency, and logging.
This separation lets you run the same cases on a local Docker-compatible engine, a CI worker, or another sandbox backend without silently changing the experiment. Record the runtime adapter and its version in every result. Also write down the trust boundary: which code is untrusted, whether the agent can reach the host, where the judge executes, and who may read credentials or traces.
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What do I need before running Docker Agent evals?
- A Docker-compatible container runtime. Docker Agent documentation also describes selecting another compatible runtime, such as Podman.
- The agent definition file and a custom evaluation directory containing versioned cases and fixtures.
- Credentials for the model/provider and any test services, scoped to the minimum permissions required.
- A pinned agent commit, evaluation-harness version, container image digest or reproducible Dockerfile, dependency lockfiles, and tool versions.
- A place to retain JSON results, logs, session data, traces, and the SQLite database produced by the run.
Capture the CLI version and host details before starting. Flags, defaults, integrations, and artifact formats can change, so the command line and version output are part of the record.
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The documented entry point is docker agent eval agent.yaml. Pass a custom evaluation directory as the second argument when your cases are outside the default location.
- Prepare the case set. Put recorded conversations, criteria, working-directory information, setup commands, and fixtures under version control. Give each case a stable identifier.
- Run the baseline command. Execute
docker agent eval agent.yaml, adding the custom eval directory if needed. Save the complete terminal output and generated artifact directory. - Filter during development. Use
--onlyto run selected cases while debugging. Do not publish a filtered run as the full benchmark; retain the filter expression in its metadata. - Control concurrency deliberately. The
-coption runs evaluations concurrently. Record the value because concurrency can affect rate limits, shared services, timing, and cost. - Repeat cases. Use
--repeatto execute each case more than once. Keep every per-repeat result, not just the aggregate. - Compare with a prior result. Supply
--baselinewith a previous run when using Docker Agent’s regression comparison. Preserve both result sets and the tolerance configuration. - Select the runtime when required. Use
--container-runtimeto choose a Docker-compatible engine such as Podman, and record its exact version.
These flags are documented interfaces, not a universal recipe. Check the versioned documentation for the installed release and archive the command’s help output alongside your results.
What isolation boundary does a Docker Agent run provide?
Docker’s evaluation documentation states that evaluations run inside containers for isolation. That boundary is meaningful only when you describe what is mounted and what the container can reach.
Declare mounts and credentials
List every bind mount, volume, temporary directory, and forwarded secret. Prefer read-only mounts for fixtures. Never assume that a workspace directory is a security boundary; a process with host access can read or alter more than the directory suggests.
Declare network policy
State whether egress is disabled, allow-listed, or unrestricted, and identify any proxy or service endpoints. Network access changes both task difficulty and reproducibility: an agent may receive different live data, encounter rate limits, or exfiltrate fixture contents.
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Account for the host-side judge
Docker Agent’s documentation notes that the agent session runs in a container while the LLM judge runs on the host. Treat that as two different trust zones. Document which traces or credentials the host-side judge can read, and ensure the judge cannot accidentally use privileged host resources while scoring.
How should I repeat runs and report stochastic variation?
Repeats characterize variability; they do not make a model deterministic. Keep the seed list, repeat count, sampling configuration, and provider response metadata with each run. If a provider does not guarantee seed behavior, record the requested seed anyway and state that it is not a determinism guarantee.
For broader benchmark claims, a July 2026 MLflow protocol recommends running every configuration across at least three seeds and reporting variance alongside the mean. That is practical guidance, not a universal statistical standard. Choose more repeats when failure rates are low, costs are acceptable, and decisions depend on small differences.
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Which metrics should an agent eval include?
Use complementary views of success. A single pass/fail number can hide unsafe tool use, inefficient trajectories, or an over-permissive judge.
| Metric view | What it answers | Implementation considerations |
|---|---|---|
| Task outcome | Did the requested end state occur? | Prefer deterministic checks on files, APIs, schemas, or database state; record partial completion and failure category. |
| Tool behavior | Did the agent choose and use tools appropriately? | Docker Agent documents tool-call F1 against expected calls. Define acceptable alternatives and inspect arguments, ordering, and error handling. |
| Relevance and quality | Is the response useful and on task? | Docker Agent supports a configurable LLM judge. Version the judge prompt, model, rubric, and threshold; validate judge decisions against human examples. |
| Response size | Was the output too short or unnecessarily long? | Docker Agent documents response-size categories. Publish category definitions and examples so the rule is auditable. |
| Trajectory and safety | What happened between request and final state? | Review full traces for loops, prompt injection susceptibility, secret exposure, destructive commands, and silent tool failures. |
| Operations | What did the run consume? | Report token usage, model and judge cost, wall time, retries, concurrency, and resource limits separately from quality scores. |
Aggregate judge rates can fluctuate. Docker Agent describes regression tolerance for baseline gates; a previously passing case that fails is still a regression. Keep that policy explicit rather than allowing an aggregate average to mask a critical case.
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What artifacts must I preserve for another team to reproduce the run?
Publish or archive a complete reproduction bundle:
- Task definitions, recorded conversations, fixtures, expected tool behavior, end-state oracles, and setup scripts.
- Agent source commit, evaluation harness commit, dependency lockfiles, tool versions, model/provider identifier, prompts, system instructions, and relevant environment variables.
- Container image digest when available, Dockerfile and build inputs, runtime and CLI versions, host operating system, CPU architecture, and resource limits.
- Judge prompt, judge model and configuration, deterministic scorer code, thresholds, and rubric examples.
- Seed list, repeat count, concurrency, timeout and retry policy, and the exact invocation.
- Per-run structured JSON, logs, session JSON, SQLite data, traces, screenshots or generated files where relevant, and cost and timing records.
- Sandbox type, mount mode, network policy, credential-forwarding rules, and the execution location of the judge.
- Known limitations, provider outages, skipped cases, and any manual adjudication.
Store immutable identifiers in the result metadata. A human-readable report is useful, but it cannot replace raw traces and machine-readable scores.
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How do Docker Agent, Inspect, and other sandbox choices differ?
Choose based on task format, scoring needs, and the trust boundary—not on a claim that one tool is universally more reproducible.
| Option | Best fit | Isolation and execution | Scoring and artifacts | Trade-offs |
|---|---|---|---|---|
| Docker Agent evals | Recorded agent sessions that map to Docker Agent’s case model. | Containerized eval sessions; Docker-compatible runtimes; the documented LLM judge runs on the host. | Tool-call F1, LLM-judged relevance, response-size categories, structured JSON, logs, session data, and SQLite. | Fast path when its session format fits; exact schemas and defaults are release-dependent. |
| Inspect | Composable agent or multi-agent evaluations and custom datasets, tools, and scorers. | Sandbox backends include Docker, Kubernetes, Modal, Proxmox, and Vagrant through extensions. | Framework components include datasets, agents, tools, and scorers; the documentation describes more than 200 pre-built evaluations. | Broader and more extensible than one CLI workflow, with more configuration to pin. |
| Agents SDK Docker sandbox client | Running sandbox work in a specified container image or isolated container environment. | Container isolation selected through DockerSandboxClient. | Use the SDK’s tracing and your own scorer; artifact completeness depends on your harness. | Requires you to design the eval dataset, repeats, and reporting layer. |
| Agents SDK Unix-local client | Trusted development or execution already protected by an external isolation layer. | On Linux it adds no OS-level confinement; commands can reach resources allowed to the host process. | Same caveat: you must build and retain the scoring and traces. | Convenient for development, inappropriate as the sole boundary for untrusted agent code. |
| Docker Sandboxes (local) | Per-agent local isolation with stronger separation than an ordinary container. | Each sandbox uses a lightweight microVM with its own Linux kernel, separated networks, and policy-controlled outbound TCP. | Use the product’s logs plus your eval harness artifacts; local-sandbox behavior does not establish cloud behavior. | Do not conflate its microVM boundary with Docker Agent’s container boundary; verify platform and policy support. |
A Docker vendor announcement dated October 14, 2026 describes Harbor integration for running eval tasks in Docker Sandboxes and configurable network policies. Treat that as a vendor claim: verify current implementation details before promising a particular integration or isolation behavior.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do I inspect failures instead of chasing a single score?
Start with the failing case and trace the whole trajectory. Classify the first meaningful failure, because a wrong final answer may be a symptom of an earlier tool or environment problem.
- Check whether setup completed and fixtures had the expected checksums.
- Verify the agent saw the intended prompt, tool schema, working directory, and environment variables.
- Inspect tool arguments, return values, retries, and timeouts for the first divergence from the oracle.
- Separate sandbox denials, network failures, provider errors, judge errors, and agent decisions.
- Re-run the case with the same image, seed, and policy, then with a fresh repeat to determine whether the problem is deterministic or intermittent.
- Update the case or oracle only when the intended behavior—not the observed behavior—has changed, and record the reason in version control.
For LLM judges, sample disagreements against human adjudication. A judge can be consistently biased, unstable across prompts, or influenced by irrelevant formatting; its output is evidence, not ground truth.
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Give readers a clean path from checkout to results: runtime and OS prerequisites, image build or digest, dependency installation, task-data revision, configuration files, exact command, seed and repeat settings, network and credential policy, expected artifact locations, and scoring commands. Include a small smoke-test case so they can verify the harness before spending money on a full run.
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State the date and version for every volatile component. Explain which services were live, which data were snapshotted, and whether model or judge responses can change after publication. If a cloud provider, model endpoint, or sandbox backend changes, publish a new result set rather than overwriting the old one.
Frequently Asked Questions
Can I call an eval reproducible if the model gives different answers each time?
Yes, if the task set, environment, configuration, scoring, and artifacts can be reconstructed and the variation is reported. Reproducibility is about the experiment record, not identical stochastic outputs.
Should I disable the network for every agent evaluation?
No. Disable or allow-list it when the task does not require external access; permit specific endpoints when live services are part of the task. In either case, document the policy because network access changes both behavior and security.
How many repeats are enough for a reliable baseline?
There is no universal count. Use enough repeats to estimate the failure-rate uncertainty that matters for your decision; for broader benchmark claims, MLflow guidance recommends at least three seeds and reporting variance, not just a mean.
Is a container safe for running an untrusted agent?
A container is an isolation boundary, not an automatic security guarantee. Review mounts, kernel and runtime configuration, capabilities, network egress, and credential exposure; use a stronger boundary such as a microVM or an externally isolated host when your threat model requires it.
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