Agentic AI Audit Logs: What to Capture and How to Replay
Agentic AI
When an agent does something wrong, can you reconstruct why. We define the audit log schema that makes agentic incidents debuggable, the storage choices that keep cost sane, and the replay pattern that turns logs into a true investigation tool.
By Arjun Raghavan, Security & Systems Lead, BIPI · July 29, 2023 · 10 min read
Audit logs are the difference between an incident and a mystery. For agentic systems, the bar is higher than for traditional services because the agent's reasoning, not just its actions, is part of the story.
What to capture
- Full prompt sent to the model, including system, context, and user sections
- Model identifier, version, and inference parameters
- Raw model response before any post processing
- Tool calls with arguments, results, and timing
- Agent and user identities, scopes, and task identifier
- Guardrail decisions and policy evaluations
Schema discipline
Use a structured schema with stable field names. Free form text logs make replay impossible. JSON lines with a documented schema and a version field is the minimum bar.
Storage choices
Hot storage for the last 30 days, cold object storage for long term. Encrypt at rest, restrict access, and apply retention policies that match your data classification and regulatory requirements.
Sensitive data handling
Prompts and responses often contain PII or secrets. Redact at log time where possible, encrypt the rest with a key that requires elevated access to use, and log access to the audit store itself.
Replay as a first class capability
Build a replay tool that takes a task identifier and reconstructs the agent's loop step by step, with the option to pause, inspect intermediate state, and rerun against an updated prompt or policy. This is how you debug, how you red team, and how you regression test.
Linking to detection
Pipe audit events into your SIEM with the same correlation identifiers used elsewhere. Anomalies in tool call frequency, scope usage, or guardrail blocks become alerts. The audit log feeds both forensics and detection.
Operational hygiene
- Test the replay tool on a real incident every quarter
- Verify retention policies match contractual commitments
- Restrict who can read the raw prompts, log every access
- Keep schema changes backward compatible or version explicitly
Logs are not for archiving, they are for answering questions. Design them around the questions you will be asked at three in the morning.
Tooling notes
LangChain and LlamaIndex both emit structured traces that can be shipped to OpenTelemetry collectors. MLflow and Weights and Biases capture eval and production runs. NeMo Guardrails surfaces policy decisions as events. Your SIEM ties it together.
Closing
An agentic system without replayable audit logs is a system you cannot defend. Capture the loop, capture the reasoning, capture the decisions, and the rest of the program has something solid to stand on.
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