An AI coding agent’s own log is useful, but it is not proof that the record is complete or faithful. For a trustworthy audit trail, correlate the agent’s events with independently captured records from the trusted harness, sandbox, identity service, network controls, and telemetry pipeline. Give every event a stable run identity, restrict execution and telemetry privileges, and retain enough linked context to reconstruct what happened without collecting more sensitive data than necessary.
Why an agent’s log is not enough
An agent can report what it believes it did, but its event stream may lack context, fail to connect actions across components, or be controlled by the same execution boundary being audited. That does not mean every agent log is deceptive. It means a log generated by the agent should be treated as one evidence source, not as independent proof.
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Microsoft Learn’s guidance on observability for generative and agentic AI calls for telemetry sufficient to reconstruct incidents, while also accounting for privacy, data minimization, residency, retention, access control, and encryption. Those requirements pull in different directions: responders need context, but prompts, outputs, retrieved material, and tool arguments may contain sensitive data.
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The design goal is therefore not simply to log everything. It is to make important actions attributable and correlatable, with critical facts recorded at boundaries the agent cannot freely rewrite.
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What an auditable run needs to record
Define an event contract before instrumenting the system. Each event should carry enough shared context to be joined to related events and interpreted later. A practical contract includes:
- Run identity: a stable request or run ID, timestamp, actor or workload identity, and the relevant agent and model identifiers where policy permits.
- Action context: the tool or command invoked, its arguments or a privacy-preserving representation, the permissions available at that moment, and the result or failure.
- Decision context: policy evaluations, approval requests and outcomes, denials, retries, and recovery or interruption state.
- Data provenance: relevant input and output context and the sources used for retrieval, subject to data minimization and access rules.
- Execution context: the sandbox or workload identity, environment, and applicable filesystem and network boundaries.
- Outcome: whether the run completed, failed, was denied, or was terminated, and the relationship between that outcome and the preceding actions.
Record timestamps consistently and preserve the run ID across the harness, execution environment, identity layer, and telemetry pipeline. Without a shared identifier, separately accurate records can still be difficult to connect into a reliable account.
Capture evidence beyond the agent boundary
Make the trusted harness responsible for recording orchestration facts: run creation, tool routing, approval decisions, state transitions, and recovery. Then capture execution facts at relevant boundaries outside the agent’s ordinary control, such as the sandbox supervisor, network proxy, identity service, or build and execution controller.
This separation follows the harness-and-sandbox model described in OpenAI’s sandbox guidance: the harness owns orchestration, approvals, tracing, recovery, and run state, while sandbox compute focuses on filesystem and command execution. It is an architectural pattern, not a guarantee that a particular deployment’s logs are tamper-proof. Corroborating records across those boundaries is a design recommendation for improving incident reconstruction; it does not require claiming that the sources prescribe a cryptographic ledger.
For a sensitive tool call, for example, the agent’s event can describe its intent and the returned result; the harness can record routing and approval; and the proxy or execution controller can record whether the request was actually allowed or run. If those accounts diverge, investigators have evidence of the discrepancy rather than a single self-reported narrative.
Constrain execution and telemetry privileges
Limit what code execution can reach
Run agent workloads in isolated compute and grant only the filesystem, tools, and permissions required for the task. Restrict outbound network access to approved destinations. Keep high-value application keys outside the execution environment; where third-party credentials are needed, broker scoped access through a trusted proxy or vault instead of exposing reusable secrets to model-directed code.
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These controls reduce the consequences of a compromised or misdirected agent and make the resulting evidence easier to interpret: network and identity records can show which bounded authority the run actually used.
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Secure the telemetry agent too
The collector or telemetry agent is part of the security boundary. OpenTelemetry’s OpAMP security guidance recommends minimum privilege and warns against automatically trusting remote configuration. It says the agent should not run as root, because a compromised agent with root access could enable broad control of its host.
Apply locally enforced limits to collection scope, files, and scripts. Validate signatures for downloaded executable packages, and treat remote configuration as untrusted until it has passed the deployment’s own checks. A pipeline that securely records application activity can still be undermined if an overprivileged collector can be redirected or replaced.
Use OpenTelemetry to connect signals, not certify completeness
OpenTelemetry provides a common approach to collecting and exporting logs, metrics, and traces. Use linked traces to follow a run across orchestration and execution steps, logs for event detail, and metrics to monitor service behavior such as latency, errors, request volume, and tool-call volume. Microsoft Learn recommends standardizing on OpenTelemetry and making telemetry sufficient for incident reconstruction.
OpenTelemetry is a shared language for signals, not proof that every relevant action was recorded. Agent-specific context still matters: provenance, permissions, tool identity, approval decisions, and the relationship between a run and its execution environment. Correlate those fields with independent control-plane records and the incident-response systems your team already uses.
OpenAI’s May 8, 2026 account of Codex telemetry describes events for prompts, approval decisions, tool results, MCP server use, and network-proxy allow or deny events. That is an example of vendor-described implementation practice, not independent evidence that every Codex deployment—or any other agent deployment—has equivalent coverage.
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Protect privacy while preserving incident context
Decide what to collect, who can read it, where it can be stored, and how long to retain it before enabling broad prompt or output logging. Prompts, tool arguments, retrieval results, and generated code can include credentials, personal information, confidential source material, or regulated data.
- Prefer recording stable identifiers, decisions, permissions, and outcomes over duplicating full sensitive payloads when the latter are not necessary.
- Where content is needed for reconstruction, define access controls, encryption, retention periods, and applicable residency requirements.
- Separate routine operational visibility from privileged forensic access when different teams need different levels of detail.
- Ensure minimization does not erase the context needed to determine which input or retrieved source influenced a consequential action.
The right level of detail depends on the system’s data and threat model. A run ID and tool name may be enough for routine metrics; investigating a harmful or unauthorized action may require controlled access to richer event context.
Test whether the evidence survives failure
Treat evidence quality as a control objective, not an assumption. Run controlled exercises and verify that each event appears in the harness record and at the relevant independent boundary, then confirm that the linked records are enough to reconstruct the run.
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- Reject an approval and confirm the decision, blocked action, and resulting run state are linked.
- Force a tool failure or agent retry and check that attempts and results are distinguishable rather than collapsed into one event.
- Terminate a sandbox and verify that the supervisor’s termination record can be connected to the harness’s recovery or failure state.
- Rotate a credential and confirm that records identify the relevant credential or scope without exposing the secret itself.
- Interrupt and resume a run, then test whether the resulting events preserve the relationship between the original request and recovered execution.
These are proposed engineering exercises, not published test results. Adapt them to the actual tools and control boundaries in the deployment.
What zero-trust observability can—and cannot—establish
A well-designed layer can make a run more attributable, expose gaps between an agent’s account and independent control records, and provide enough linked evidence to support incident response. It cannot establish completeness merely by using OpenTelemetry, collecting more events, or placing a “zero trust” label on the pipeline. Coverage depends on which boundaries are instrumented, whether identities and timestamps correlate, whether the records are protected, and whether retention preserves the evidence responders need.
NIST’s AI Agent Standards Initiative announcement of February 17, 2026, frames secure operation and interoperability as standards challenges for autonomous agents that can write and debug code. It is useful context for why portable, well-correlated telemetry matters; it is not itself a settled technical control or evidence that a particular standard has been adopted.
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