What to Log in an AI Agent Without Collecting Too Much

Agent observability creates a tension. Teams need enough detail to debug and audit behavior, but not so much that logs become a second uncontrolled copy of sensitive business data.

011. Log the Decision Path

Capture the user intent, selected workflow, model version, tool calls, tool results status, approvals, refusal events, and final outcome. These fields explain what happened without always storing every raw document.

When raw content is necessary for debugging, store it intentionally with access control and retention limits.

Agent logs should explain behavior without becoming uncontrolled data stores.

022. Redact Before Storage

Credentials, personal data, payment details, secrets, and restricted customer information should not casually appear in logs. Redaction should happen before storage, not only in the viewer.

If the system cannot safely redact a category, decide whether that content should enter the agent workflow at all.

033. Keep Trace IDs Across Systems

Agent workflows often cross APIs, databases, queues, and user interfaces. A trace ID connects those events so the team can reconstruct the path without searching manually across systems.

This is especially important when a tool succeeds but the user-visible workflow fails later.

044. Define Retention by Risk

Not all logs deserve the same lifetime. Security events, approvals, and financial actions may need longer retention than ordinary low-risk drafts.

Logging should help the business operate the agent, not create a permanent archive of everything users ever typed.