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If an AI agent passes an evaluation locally but fails inside a microVM, treat the mismatch as a clue—not proof that the hypervisor is broken. Compare the same case across both executions, preserve the failed run, and identify which boundary changed: startup, files, environment variables, network access, credentials, architecture, or resource limits.
The exact commands and controls depend on your evaluation framework and microVM provider. The workflow below stays provider-neutral, with Docker Agent, Vercel, Firecracker, and AgentENV examples clearly labeled where their documented behavior is relevant.
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Why can an eval pass locally but fail in a microVM?
A local pass and a microVM failure show that the two executions are not equivalent in some consequential way. The difference might be as small as a missing fixture or an environment variable that was never forwarded; it could also be a failed guest boot, network policy, dependency setup, timeout, or a difference in the evaluator itself.
“Inside a microVM” describes an execution boundary, not a single configuration. Filesystem mounts, network routes, credential handling, startup procedures, snapshots, and resource limits vary by platform. Even when the agent code and prompt are identical, the environment that delivers tools and dependencies may not be.
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Start by separating three kinds of failure:
- Execution failure: the guest or agent never reaches the expected tool call or response. Look first at boot, setup, dependencies, process state, and timeouts.
- Environment-dependent behavior: the agent runs, but a file, endpoint, executable, or credential it can use locally is absent or inaccessible in the guest.
- Evaluation or scoring difference: the agent appears to behave as expected, but the recorded result differs. Check the evaluation artifacts and whether the evaluator or judge runs in a different environment.
These categories can overlap. A missing network response can look like an agent reasoning failure, and an unavailable fixture can lead to a valid but unexpected tool sequence. Use the execution trace and guest evidence to locate the earliest point where the failing run diverges from the passing one.
What should I capture before changing anything?
Pin one failing case and record enough detail to reproduce it. Changing multiple settings before collecting a baseline can hide the cause or make the problem disappear without showing what fixed it.
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- Case identifier, full input, and expected outcome or evaluation criteria.
- Expected tool calls, if the eval checks them, and the actual tool-call sequence.
- Agent configuration, evaluator version, and timestamps.
- Image digest or template identifier, plus the setup sequence used to create the environment.
If the harness supports repeats, run the same case more than once and save each result. A consistent failure points toward a deterministic difference; inconsistent outcomes call for separating flaky infrastructure or changing model/judge behavior from stable environment defects. Docker Agent documents repeat runs and comparison against a saved baseline, but those features are an example of one workflow, not a requirement for every harness.
Keep the failed run intact
Do not let automatic cleanup erase the evidence before you can inspect it. Docker Agent documents a --keep-containers option and describes eval output that includes a log, JSON results, session exports, and a SQLite session database. Those names and controls are specific to that tool; other systems may expose different artifacts or retention settings.
Preserve both sides of the execution story: infrastructure and guest logs, plus the full agent conversation and tool-call history. A log showing a healthy VM does not establish that the agent saw the right files or successfully reached a service. Conversely, a failed tool call in the trace may point to a boundary problem rather than an eval-scoring defect.
How do I compare the passing and failing execution envelopes?
Make a side-by-side record of the dimensions that can change what the agent sees or what the evaluator measures. Keep the case, agent configuration, and setup sequence constant wherever possible. After documenting the baseline, vary one dimension at a time.
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|---|---|---|
| Case and agent | Input, agent settings, tool definitions, evaluator version, and expected criteria | A changed case or evaluator can create a pass/fail difference unrelated to the guest. |
| Image and setup | Image or template identifier, dependency versions, startup command, and setup scripts | Different base images or incomplete setup can alter available programs and libraries. |
| Architecture and boot | Host and guest CPU architectures, guest kernel or base image, and boot/start evidence | Incompatible architecture or an incomplete boot can prevent the eval from reaching agent behavior. |
| Workspace | Working directory, mounted paths, access modes, and whether files are shared, cloned, or absent | The guest may not see the same fixtures, configuration, or write locations as the local process. |
| State lifecycle | Fresh start, resumed environment, or snapshot-restored environment | A restored or layered environment may not have the same state as a clean execution. |
| Environment and credentials | Names of variables present, how values are delivered, and whether a credential proxy is used | Host processes may receive values that are not inherited by the guest process. |
| Network | DNS behavior, proxy route, outbound policy, and allowed or denied destinations | A host-side request can succeed while an equivalent guest request is blocked or routed differently. |
| Resources and timing | CPU, memory, disk, and timeout settings | A constrained run can fail during setup or execution even if the local run completes. |
| Evaluator placement | Where the agent, evaluator, and any judge execute | A host-side judge can succeed independently of the guest agent’s access to services. |
This comparison is a diagnostic method, not a guarantee that every platform exposes every setting. Record “unknown” rather than assuming two runs match. Once a difference is found, change only that dimension and rerun the pinned case; if that does not explain the result, restore the baseline and test the next candidate.
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How do I inspect the failed guest or container?
Retain the failed environment when your platform supports it, then inspect the guest state rather than relying only on host-side assumptions. The exact command or UI varies by orchestrator and guest operating system, so do not assume Docker Agent’s container-retention flag applies to another microVM stack.
- Identify the retained execution. Match the guest or container to the saved case ID and timestamp so you do not inspect a different run.
- Check process and startup evidence. Determine whether the setup process completed, whether the agent process started, and whether expected services or helper processes are running.
- Check the current working directory. Compare it with the path expected by the eval and agent configuration.
- Inspect expected files and executables. Confirm fixtures, configuration, scripts, and dependencies exist at the paths the agent uses.
- Check ownership and access. Verify that the guest process can read required inputs and write expected outputs. Look for permission errors, broken symlinks, or names that differ by letter case.
- Compare the trace with logs. Find the first failed setup action or tool call, then correlate its timestamp with guest and infrastructure events.
Use the runtime’s documented inspection method for the specific environment. Docker Agent recommends inspecting a retained container using the selected container runtime; that is a Docker Agent example, not a universal microVM interface. Avoid dumping all environment values or attaching unredacted process output to a bug report: credentials and tokens can appear in them.
Could the guest have different files or workspace state?
Yes. A host directory visible to a local run is not necessarily the guest’s workspace, and different sandbox modes expose different files and write behavior. Verify the actual guest paths and contents instead of inferring them from the host checkout.
Direct mount, private clone, or no mount
Docker Agent documents workspace modes that include a direct workspace mount, a private clone, and a sandbox without a workspace mount. These modes are materially different: a direct mount exposes the selected host workspace, a private clone gives the run its own copy, and a mountless environment cannot be expected to see the host workspace unless another mechanism supplies those files.
For the failing case, establish which mode was used and where it places files. Check whether the fixture is part of the mounted or cloned content, whether setup runs before the agent starts, and whether the agent’s configured working directory points to the right location. A local test that implicitly reads a file elsewhere on the host can pass while a guest correctly reports that the file is missing.
Fresh state versus restored state
Ask whether the failing run started clean, resumed an existing environment, or came from a snapshot. Layered images and snapshots can make setup faster or preserve prior state, but the relevant debugging question is whether the evaluated run received the state you intended. AgentENV describes layered images and snapshots; behavior should not be generalized from that implementation to every sandbox platform.
To isolate stale or missing state, compare a fresh execution with the normal lifecycle while holding the case and image constant. If only one lifecycle reproduces the failure, inspect what setup ran before capture or what state the resume path restores. Do not interpret a success after a fresh start as proof that the hypervisor was faulty; it establishes a state-lifecycle difference that still needs to be accounted for.
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How do I test network access and credentials from inside the guest?
Test connectivity from the guest process’s execution boundary, using the route and identity the agent actually uses. A successful host-side request does not show that guest DNS, proxy configuration, outbound policy, or credentials are correct.
Check the route before the application behavior
First establish whether the guest can resolve and reach the specific endpoint required by the case. Then inspect whether requests use a proxy and whether sandbox network policy permits the destination. A failure to resolve a name, connect through a proxy, or reach a denied destination can all produce similar application-level symptoms, so record the concrete failure stage rather than summarizing it as “network is broken.”
Keep the check narrow: test only endpoints required by the reproduction, and use the same route as the agent. A host’s unrestricted access is not a reliable substitute for this guest-side check when network rules differ.
Verify credential delivery without exposing secrets
Check whether the required credential variable or credential mechanism is available to the agent process, but record only whether it is present—not its value. Also distinguish credentials used by the judge from those used by the agent. Docker Agent’s documentation describes an architecture in which credentials may be injected through a host-side proxy. In that arrangement, the host-side judge can authenticate while the isolated agent still lacks an equivalent path.
Vercel’s sandbox documentation says environment values are supplied through an SDK option rather than inherited automatically. That is a Vercel-specific behavior, not a rule for all microVMs. Check the relevant provider’s documented forwarding or proxy mechanism before concluding that environment variables should cross the guest boundary automatically.
What if the eval fails before the agent starts?
If there is no meaningful agent trace, investigate guest startup, architecture, and setup before debugging prompts or scoring. A timeout can be a boot or initialization problem rather than a slow agent response.
Confirm architecture compatibility
For Firecracker-based deployments, the guest operating system architecture must match the host architecture. Check the actual host and guest architecture used by the failing run rather than relying on a local development machine’s assumptions. This constraint is specific to the cited Firecracker guidance; verify the compatibility rules for other VMMs separately.
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Use boot evidence to locate the failure
Firecracker’s FAQ advises checking the InstanceStart response and kernel command line when guest console output is missing. Review those alongside VMM and guest boot logs to determine whether the guest started and produced usable console output. If the failure is earlier than agent initialization, an eval result alone will not tell you why the guest failed to become ready.
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Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do I tell an agent failure from a judge or scoring failure?
Compare the recorded agent behavior with the evaluation result, and establish where each component ran. A score is an outcome of the evaluation pipeline; it is not by itself proof that the guest agent behaved incorrectly.
Docker Agent documents an arrangement where agent execution is isolated while the judge runs on the host. That placement means a judge can have working authentication or network access even when the guest agent does not. Inspect session exports or equivalent artifacts to see what the agent actually received and returned, then compare that evidence with the judge’s inputs and result.
When repeated runs differ, retain separate agent traces and judge outcomes. An LLM judge can vary between runs, so a changed score does not necessarily mean the sandbox execution changed. Conversely, a repeatable missing file or failed request in the agent trace is concrete execution evidence even if the judge result is noisy.
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How do I make a minimal reproduction and prevent a regression?
Reduce the case until it contains the smallest prompt, tool set, fixture, and setup that still fails. A compact reproduction makes it easier to distinguish an agent behavior issue from a boundary or initialization difference.
- Start with the pinned failing case and retain its complete trace.
- Remove unrelated tools, files, and setup actions one at a time while keeping the failure-triggering path intact.
- Record the exact image or template, runtime and dependency versions, architecture, workspace mode, network policy, and relevant resource settings.
- Sanitize logs and include the first divergent event, not only the final timeout or score.
- Repeat from the same lifecycle state, and note whether a fresh run behaves differently.
- When one changed boundary dimension resolves the issue, encode that requirement in the eval setup or deployment contract and keep the minimized case as a regression test.
A baseline comparison or gate is one way to detect future changes; Docker Agent documents such a workflow, but the general safeguard is to preserve a reproducible case and the environment assumptions it depends on. The useful outcome is not merely a passing run: it is a known execution contract that makes the pass reproducible.
Which boundary should I investigate first?
Use the earliest observable divergence to choose the next check. The table is a triage aid; it does not assign a provider-independent cause to any symptom.
| Observed symptom | First boundary to inspect | Evidence to collect |
|---|---|---|
| No agent trace or tool call | Boot and initialization | Start response, boot logs, setup completion, process state |
| Agent reports a missing file | Workspace and working directory | Guest path, file listing, mount or clone mode, permissions |
| Tool request fails before a response | Guest network route | DNS result, proxy route, outbound policy, request failure stage |
| Host succeeds but guest authentication fails | Credential boundary | Credential presence indicator, forwarding configuration, proxy path, which process authenticates |
| Setup works in fresh runs but not resumed runs | Snapshot or lifecycle state | Fresh/resumed status, setup sequence, state at capture and restore |
| Trace looks successful but score changes | Evaluator and judge | Agent trace, judge inputs and placement, repeat outcomes |
| Guest never emits expected console output | Boot configuration | For Firecracker, the InstanceStart response and kernel command line |
Do not change several boundaries at once just to obtain a pass. A useful diagnosis explains why the local and guest executions differed and which setting, artifact, or lifecycle assumption must remain consistent.
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Frequently Asked Questions
Does a microVM-only eval failure mean the hypervisor is defective?
No. The fact that the failure is limited to a microVM narrows the comparison, but it does not identify the faulty component. The agent, guest setup, network policy, workspace mapping, judge placement, or startup configuration may differ even when the VMM is functioning as designed.
Should I share a complete failed-run environment with a bug report?
Share the smallest reproducible case and sanitized logs rather than a raw environment dump. Environment variables, process arguments, session exports, and databases can contain credentials or sensitive prompt data; remove those values while preserving variable names, timestamps, and the failure sequence.
Can an eval pass locally and still be incorrectly configured?
Yes. A local pass only establishes that the tested local execution met its criteria; it does not establish that the eval’s expected files, credentials, or network route are part of the guest’s execution contract. The local environment may be supplying hidden dependencies that the sandbox never receives.
What details should I include when asking a provider for help?
Include the case ID, timestamp, image or template identifier, runtime versions, host and guest architecture, lifecycle mode, and the earliest failing event. Add sanitized logs and state whether the same minimized case reproduces from a fresh run; avoid sending secret values or unnecessary customer data.
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