How to Audit AI Agent Decisions and Actions
Make every agent decision traceable and defensible with end-to-end traces, validators, approvals, immutable storage, and incident playbooks.
Executive Summary
Auditing an AI agent means proving what it knew, decided, and did—plus who approved it. Record inputs, retrieved context, tools called, outputs, costs, latency, policy results, approvals, and outcomes for every decision. Use correlation IDs, immutable storage, pre-/post-action validators, and reason codes for human overrides. Review trails on a cadence with clear rollback and incident workflows.
Audit Essentials Checklist
Audit Implementation Process
| Step | What to do | Output | Owner | Timeframe |
|---|---|---|---|---|
| 1 | Define audit scope & risk tiers for each decision | Decision catalog + tiers | Product/Risk | 2–3 days |
| 2 | Instrument tracing SDKs & correlation IDs | Event schema + IDs | Platform/MLOps | ~1 week |
| 3 | Add validators and approval routes | Gate checks + routes | Security/Risk | 3–7 days |
| 4 | Configure immutable storage and retention | Append-only store + index | Data Eng | ~1 week |
| 5 | Publish review & incident SOPs | Playbooks + roles | Ops | 3–5 days |
| 6 | Report KPIs; backlog improvements | Dashboards + tickets | AI Lead | Ongoing |
Do / Don’t
| Do | Don’t | Why |
|---|---|---|
| Use correlation IDs end-to-end | Rely on ad-hoc logs | You need reconstructable timelines |
| Capture inputs, tools, costs, outcomes | Log only final text | Root-cause needs full context |
| Gate risky actions with approvals | Auto-run all tools | Reduces impact of bad calls |
| Store immutable traces | Allow edits/deletes | Preserves evidence integrity |
| Review incidents on cadence | Fix one-offs only | Turns findings into controls |
Deeper Detail
Instrument every decision with a standardized trace: input snapshot, retrieval citations, tool names and parameters, outputs, costs, latency, validator results, approver identity, and final outcome label. Add pre-execution validators (schema checks, RBAC, PII guardrails) and post-execution validators (result checks, anomaly thresholds). For elevated-risk actions, route to human approvers and require reason codes for approvals and overrides.
Back traces with append-only storage and indexed search. Build dashboards for audit KPIs and fast incident response. On incidents, freeze artifacts, run a blameless review, and update prompts, validators, or datasets. Rollback should be one click via feature flags or workflow cancel/resume.
TPG POV: We productize decisions with clear contracts and observable behavior—governance that scales autonomy without slowing delivery.
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Frequently Asked Questions
Input snapshot, retrieval sources, tool calls with parameters, outputs, validator results, costs, latency, correlation ID, approver (if any), and outcome label.
Store document IDs, versions, snippet offsets, and citations in the trace so you can replay decisions with the exact context.
Use an immutable, indexed store (append-only or WORM) with role-based access and defined retention policies.
Redact or tokenize on ingestion, segregate re-identification keys, and log access to sensitive fields.
Risk-tier rules such as sensitive data, high cost, external execution, low confidence, or failing validators.