The Complete Guide to AI Agent Audit Logs

When your AI agent makes a decision, can you prove what it did and why? Audit logs are the answer. As we saw in the Moltbook breach, comprehensive logging is essential for security.

Why Audit Logs Matter

• Compliance — Regulations require proof of AI decisions (see our enterprise governance framework)
• Debugging — Understand why an agent behaved unexpectedly
• Accountability — Know who (or what) did what, when
• Trust — Show stakeholders your AI is governed with proper permissions

What to Log

A comprehensive audit log should capture:

{ "timestamp": "2026-02-02T14:30:00Z", "agent_id": "agent_abc123", "action": "email.send", "scope": "email.send", "target": "ceo@company.com", "input": {"subject": "Q4 Report", "body": "..."}, "status": "approved", "latency_ms": 45, "approval": { "required": true, "approved_by": "user_xyz", "approved_at": "2026-02-02T14:29:55Z" }, "metadata": { "ip": "192.168.1.1", "session_id": "sess_123" } }

Log Levels

1. Action logs — Every action attempted
2. Decision logs — Why action was allowed/blocked
3. Approval logs — Human approval workflow
4. Error logs — Failures and exceptions

Immutable Logs with Blockchain

Agent Shield anchors audit logs to blockchain (Polygon), making them tamper-proof. Learn more about why we use blockchain for audit logs and how it provides cryptographic guarantees:

• Tamper-proof — No one can modify history
• Verifiable — Anyone can verify the hash
• Timestamped — Cryptographic proof of when

Querying Logs

from agentshield import AgentShield shield = AgentShield(api_key="...") # Get all actions by an agent logs = shield.audit.query( agent_id="agent_abc123", start_date="2026-02-01", end_date="2026-02-02" ) # Get blocked actions blocked = shield.audit.query(status="blocked") # Export for compliance shield.audit.export(format="csv", path="audit_feb.csv")