How do I monitor AI agents in production?

Monitoring AI agents in production requires observing what agents DO (actions), not just what they SAY (outputs) — and standard APM tools like Datadog and New Relic can't see inside the agent decision loop.

Traditional monitoring misses the critical signal: the agent's proposed actions. You can monitor HTTP latency, error rates, and token usage — but none of that tells you if an agent just proposed DROP TABLE or tried to exfiltrate PII to an external API.

Effective AI agent monitoring requires:

• Action-level visibility: Every tool call, database query, API request, and file operation logged with full parameters
• Policy evaluation audit: Which rules passed, which blocked, what was the evaluation latency
• Anomaly detection: Agent behavior baselines with alerts when action patterns deviate
• Cross-agent correlation: In multi-agent systems, tracking which agent influenced which action
• State drift detection: Alerts when agent state diverges from expected patterns

Exogram's Trust Ledger captures every evaluation — pass and block — with SHA-256 state hashes, execution tokens, and full action payloads. Export to your existing SIEM (Splunk, Datadog, ELK) via webhook. The governance layer doesn't replace your monitoring stack — it provides the signal your stack can't generate.