Why traditional monitoring falls short
Traditional application monitoring assumes relatively deterministic execution. Agentic systems introduce another layer of uncertainty because the system can make different decisions for similar inputs.
Signals that explain agent behavior
Observability therefore needs to capture more than errors and latency. We need to understand which tools were selected, what information influenced decisions, where uncertainty appeared, and how the agent recovered from failures.
From experiments to production
These signals give engineering teams a much clearer picture of how an agent behaves once it moves from controlled experiments into production.