AI Agent Observability: Logs, Traces, and Evals

In production, something will go wrong.

The question is how fast you can answer:

• what happened?
• why?
• how to fix it?

That’s observability.

What to Log

• sanitized task input
• plan summary
• tool calls + parameters
• step outputs
• final output
• escalation reasons

Tracing

You want a tree of execution: task -> steps -> tool calls -> verification.

Evals

Treat evals as tests:

• scenario sets
• acceptance criteria
• regression gates

Every production failure should become a new eval.