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An AI agent’s own event log is useful evidence, but it cannot prove by itself that every important action was recorded faithfully. To audit autonomous code execution, correlate the agent’s events with records made by the trusted harness and independent execution boundaries—such as the sandbox supervisor and network proxy—then link them with stable run identifiers and timestamps. This helps expose missing context or disagreement between records; it does not make any log tamper-proof.
Why an agent log is not enough
An event log can be accurate as far as it goes and still leave an incomplete account. It may omit the permissions behind a tool call, the source of retrieved information, an approval decision, or what happened after a tool returned. It may also be controlled by the same agent or execution boundary whose actions you need to audit. A record from that boundary alone cannot independently establish what it failed to capture.
Microsoft Learn’s guidance for generative AI and agentic AI observability calls for telemetry sufficient to reconstruct incidents, while balancing that need against privacy, data minimization, residency, retention, access controls, and encryption. The practical goal is therefore not to collect everything indiscriminately. It is to record enough linked context to answer who or what acted, on which run, with what authority, and with what result.
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OpenTelemetry (OTel) provides a shared approach to collecting and exporting logs, metrics, and traces. It makes signals easier to correlate across systems, but standardization does not establish that a record is complete or truthful. Agent-specific context and corroborating records from outside the agent’s control are still needed.
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What an auditable execution record needs
Give each run a stable identifier and carry it through the harness, agent events, tool activity, sandbox, and relevant control-plane telemetry. Use timestamps consistently, and record enough identity and provenance to distinguish actors and inputs. The exact fields depend on the task and privacy policy, but a useful event contract includes:
- Run and actor context: run or request ID, workload or actor identity, timestamp, and agent or model identifier where policy permits.
- Input and provenance: relevant request and response context, plus the sources of retrieved information. Apply minimization rather than retaining sensitive content without a defined need.
- Tool activity: tool name, invocation, relevant arguments, permissions in effect, result, and failure or retry state.
- Control decisions: approval requests and outcomes, policy decisions, and tool-routing events.
- Execution context and outcome: environment or sandbox identity, relevant boundary events, and the run’s final or interrupted state.
Link related events into an end-to-end trace so an incident responder can follow a run across components. Use logs for detailed events, traces for the relationship between steps, and metrics for aggregate behavior such as latency, errors, token usage, and tool-call volume. These signals answer different questions; a metric showing an unusual call volume does not replace the events needed to inspect a particular run.
Which boundaries should record what happened?
Design for corroboration: important actions should leave evidence in more than one place when the architecture allows it. The following is a recommended division of responsibility, not a guarantee provided by a particular product.
| Record source | Useful evidence | What it cannot establish alone |
|---|---|---|
| Agent event stream | Agent-visible steps, tool requests and results, and relevant input or response context. | That the stream includes every action or was not changed within the agent’s control. |
| Trusted harness | Orchestration, tool routing, approvals, run state, and recovery state. | Every fact about what occurred inside the execution environment or on the network. |
| Sandbox supervisor or execution controller | Execution-boundary facts, such as sandbox lifecycle and command or filesystem activity captured by that boundary. | Agent intent or the meaning of an action without linked run and tool context. |
| Network proxy or egress control | Allowed or denied outbound network activity visible to that control. | What happened outside the controlled network path or why a request was made. |
| Telemetry pipeline | Collected and exported signals, their correlation, and pipeline health where instrumented. | Events never emitted or captured upstream of the pipeline. |
The harness/sandbox separation described in OpenAI’s sandbox guidance is a useful architectural pattern: keep orchestration, approvals, tracing, recovery, and run state with the trusted harness, while sandbox compute focuses on filesystem and command execution. This separation makes it easier to compare the harness’s account with execution evidence. The specific independent capture points will vary by deployment.
How to build the observability layer
1. Define the event contract and handling rules
Decide which events are necessary to investigate a failure or policy violation before collecting prompts, outputs, or tool arguments. Define the run ID, identity fields, timestamp conventions, provenance fields, and tool-event shape. Set retention, access, encryption, and residency rules for the resulting data; observability records can themselves contain sensitive material.
2. Emit control-plane records from the trusted harness
Record orchestration events, approvals, tool routing, run-state changes, and recovery actions in the harness. Carry the run ID into calls to tools and into the relevant boundary records. A harness record should be compared with the agent’s account, not treated as a reason to stop collecting execution-side evidence.
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3. Capture execution and network facts outside the agent
Use the sandbox supervisor, execution controller, proxy, or other trusted controls to capture facts at boundaries the agent cannot freely edit. Isolate workloads, narrow filesystem and command access to the task, and restrict outbound connections to approved destinations. Keep high-value application credentials outside the execution environment; where a task needs third-party credentials, provide scoped access through a trusted proxy or vault.
4. Secure the telemetry agent and its configuration
Telemetry infrastructure is part of the security boundary: a collector with broad machine access or unsafe remote configuration can become another route to compromise. OpenTelemetry’s OpAMP security guidance recommends minimum privilege and a zero-trust approach to remote configuration. It advises against running an agent as root, recommends locally enforced limits and restrictions on files or scripts, and calls for validating signatures on downloaded executable packages. Treat remote offers as untrusted unless independently validated.
5. Correlate signals and monitor for drift
Export interoperable logs, traces, and metrics, preserving the run identifier and relevant identity context across components. Monitor service-specific measures such as latency, error rate, token usage, and tool-call volume. Establish behavioral baselines and alert on meaningful deviations, while keeping sufficient linked context to investigate an alert under the organization’s retention and privacy rules.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to test whether the evidence is useful
Make evidence quality an engineering control objective. In controlled exercises, trigger conditions that should produce distinct records, then verify that the run can be reconstructed from linked events and that the expected independent boundary recorded its part. Useful cases include:
- A denied network request, checked against both the harness context and proxy or egress record.
- A rejected approval, checked against the approval decision and the absence of the unauthorized action where that can be observed.
- A failed tool call or retry, checked for invocation, result, and run-state transitions.
- A sandbox termination or interruption followed by recovery, checked against supervisor and harness state.
- A credential rotation, checked to ensure the old credential is no longer usable and that the access change is represented in the relevant control records.
For each exercise, note which record should exist at each boundary and how records are joined. A missing event, inconsistent run ID, unexplained gap in time, or disagreement between sources is a finding to investigate—not a reason to assume the most convenient record is correct.
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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesHow to choose and assess a design
When reviewing an observability design, evaluate whether critical actions are captured outside the agent’s control; whether identity, provenance, and trace correlation survive across components; and whether execution privileges, filesystem scope, and egress are restricted. Also check credential handling, approvals and recovery, privacy and retention safeguards, and interoperability with OTel and incident-response systems. This is a design review framework, not a vendor ranking.
NIST’s AI Agent Standards Initiative announcement of February 17, 2026 frames secure operation and interoperability as standards challenges as agents take on tasks such as writing and debugging code. It is useful context for why portable, well-correlated evidence matters, not evidence that a particular control or standard has already been adopted. Similarly, OpenAI’s May 8, 2026 account of its Codex sandbox describes agent-native telemetry for prompts, approval decisions, tool results, MCP server use, and network-proxy allow/deny events. That is an implementation example from the organization describing its own system, not independent proof that another deployment has equivalent controls.
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