You don’t know what your agent will do until it’s in production

Monitoring AI agents in production is fundamentally different from traditional software because of their infinite natural language input space and non-deterministic behavior. Effective agent observability requires capturing full conversational trajectories and intermediate steps, not just system metrics. To overcome the challenge of scaling evaluation, teams can combine focused human review in annotation queues with automated LLM-based evaluators. These tools help identify usage patterns and failure modes, forming a foundation for the continuous improvement of production agents.