Short answer: To audit AI agent API calls, record the agent and user identity, requested tool, parameters, policy decision, approval, credential reference, redaction, downstream outcome, and timestamp for every execution.
Why ordinary API logs are incomplete for agent activity
A downstream API log may identify a service account and endpoint, but it often cannot explain which agent selected the action, which user initiated the workflow, what policy was evaluated, or whether sensitive output was removed.
Agent investigations require a connected execution story. Teams need to trace intent, authorization, credential use, API outcome, and returned data without storing raw secrets in the evidence itself.
What a useful agent audit record should contain
- Identity. Agent, initiating user or workload, session, and policy assignment.
- Intent. Named tool, target resource, parameters, and requested environment.
- Decision. Policy version, allow or deny result, reason, risk score, and required approval.
- Credential reference. The vault path or credential identifier used, never the secret value.
- Execution result. Downstream status, duration, retries, timeout, rollback, and error details.
- Data handling. Classification and redaction rules applied before output reached the agent.
How KeyRunner captures live agent audit logs
KeyRunner captures the agent execution path live, including tool discovery, the requested action, policy decision, approval state, credential reference, API result, and applied response controls. Enterprises can see which agent is doing what as activity occurs.
Because the audit event is created at the governed execution layer, it connects agent identity and policy intent with the downstream API call. Logs can remain in customer-managed infrastructure and feed existing SIEM and monitoring workflows.
Enterprise checklist
- Stable agent and session identity
- Named tool rather than only endpoint
- Policy decision and reason
- Credential reference without secret value
- Response redaction evidence
- Exportable and tamper-resistant records
Frequently asked questions
How do you audit API calls made by AI agents?
To audit AI agent API calls, record the agent and user identity, requested tool, parameters, policy decision, approval, credential reference, redaction, downstream outcome, and timestamp for every execution.
Are API gateway logs enough for AI governance?
Usually not. Gateway logs describe traffic, while agent governance also needs agent identity, tool intent, policy, approval, and model-facing data controls.
Does KeyRunner log raw credentials?
No. Audit records identify the credential by reference while keeping its secret value out of agent context and logs.
Related KeyRunner guides
- How Do AI Agents Access APIs Without Exposing Credentials?
- What Is MCP Security and How Do You Govern MCP Tools?
- How Do You Enforce Least Privilege for AI Agents?
- Agentic Security and AI Governance Guide
Explore the KeyRunner secure agent runtime, step through the live governance scenarios, or talk with the KeyRunner team.