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What an audit should let you establish
For a tool action, an investigator should be able to identify who or what initiated the run, which agent and tool acted, what resource and operation were involved, which policy or approval decision applied, and whether execution succeeded, failed, or was denied.
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Capture enough information to reconstruct the event, but decide deliberately whether to log tool arguments or returned content: either may contain secrets or personal data. A useful record normally includes:
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- The initiating user or workload identity, agent or run identifier, tool or server name, and execution principal.
- The operation and target resource.
- The policy or human-approval decision and the execution status, with an error or denial reason where appropriate.
OpenAI’s Codex security guidance describes telemetry for tool approval decisions, execution results, MCP server use, and network proxy allow-or-deny events. AWS recommends audit trails of agent decisions and actions. These examples support logging decisions and outcomes, not merely a list of tools: OpenAI’s Codex security guidance and AWS Prescriptive Guidance on agent governance.
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Why agent logs and service audit logs both matter
Agent-side telemetry can explain why a tool was called and what approval or policy decision preceded it. A downstream service’s audit log can show activity at the resource that actually received the request. Either view can be incomplete on its own: a framework trace may not prove what the service executed, while a cloud record may not explain the agent’s decision.
Do not confuse administrative audit records with agent tool-call telemetry. OpenAI’s API Platform Audit Logs API documents organization and configuration activity and separates those records from API request and response content. OpenAI’s Codex guidance describes execution telemetry instead. Choose evidence sources according to the question being investigated; these logs have different scopes.
For cloud workloads, AWS identifies CloudTrail and CloudWatch among the services used to monitor agent tool usage. Google Cloud documents resource audit logs. Correlation is more useful when the agent event and downstream record share a run, request, or other correlation identifier and preserve meaningful identity context. See AWS guidance on secure agent access and usage and Google Cloud Audit Logs overview.
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Implement logging and access controls in six steps
1. Map every route to a resource
Inventory each agent, tool, MCP server, API, and sensitive resource it can reach. For every connection, note the credential or principal used, who can grant or change its permissions, and which system records the final operation. When a tool acts for a user, preserve that initiating identity through the agent chain where the platform supports it. AWS describes identity propagation, permission boundaries, IAM, and Secrets Manager as parts of agent governance: AWS Prescriptive Guidance.
2. Define the event before choosing a dashboard
Specify the fields each attempted action must emit: timestamp, initiating identity, agent or run ID, tool or server, operation, target, policy or approval result, status, and correlation ID. Include error or denial detail when it helps explain the outcome. Decide whether payloads are necessary for your audit purpose and apply appropriate safeguards if they may hold sensitive data.
3. Enforce permissions where the action takes effect
Put authorization at the boundary that performs the operation, not only in the agent’s prompt or user interface. Scope each tool’s credentials to the required resources and operations; separate read access from write or destructive access where practical. Keep credentials in managed secret storage, consider human approval for consequential actions, and define a circuit breaker for abnormal patterns. AWS calls for permission boundaries, least-privilege tool scope, identity propagation, and circuit breakers in its agent governance guidance and secure agent access guidance.
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4. Verify which events are actually captured
Test representative allowed, denied, approved, and failed actions. Confirm that both the runtime and the downstream service produce the evidence you expect, and check that the relevant roles can read it.
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Logging defaults differ by service. Google Cloud’s Agent Platform documentation says Admin Activity and System Event logs are always enabled, while Data Access logs are disabled by default, with a stated BigQuery exception. Do not assume that agent data-access events are covered: verify the configuration for the specific service and project. Google’s Agent Platform audit logging information describes those defaults.
5. Restrict access to logs and retain the evidence
Limit who can read audit data; where practical, separate log administration from agent administration. Google Cloud distinguishes Logs Viewer from Private Logs Viewer for access to Data Access logs in the _Default bucket; permissions determine which audit-log types a user can view. Check the applicable roles in the Cloud Audit Logs overview.
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Set retention, export, integrity, and access-review requirements before relying on a provider’s log endpoint as your only record. OpenAI says API Platform audit logs have no fixed retention period and are not guaranteed to remain permanently available; customers that need long-term retention should export and store copies. See the OpenAI Admin and Audit Logs API documentation.
6. Reconcile runtime and downstream records
Periodically compare agent-side tool events with audit records from the service that executed the operation. Investigate downstream actions with no matching agent event, agent actions lacking a policy decision, and identity or correlation fields that disappear between layers. Alert on patterns your risk model treats as significant, such as unexpected tool use, unusual denials, permission changes, or abnormal activity.
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Use these questions to assess an agent framework, gateway, cloud logging service, or combined design. They are practical comparison criteria, not a vendor-neutral certification checklist.
Quick Recap
- Event coverage: Does it capture requests, approvals, policy allow-or-deny decisions, execution results, and downstream resource access?
- Enforcement point: Does authorization happen in application middleware, a gateway or interceptor, cloud IAM, the service itself, or more than one layer?
- Identity attribution: Can the evidence distinguish the initiating user, agent, delegated agent, tool, and execution principal?
- Evidence access: Which roles can read administrative, system, denied, and data-access events?
- Retention and export: What availability and export paths are documented, and can you control retention and deletion?
- Correlation and response: Can you link runtime traces to infrastructure events and alert on relevant anomalies?
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