Agent Observability
Observing an agent means tracing every step of its loop — each decision, tool call, and intermediate result — not just the final answer.
Prerequisites
Overview
When an agent produces a wrong or strange result, the only way to actually debug it is to see the full trace — every reasoning step, every tool call and its result, in order. Without that, an agent is a black box.
Where It Fits
Agent Run
Trace Collector
Observability Dashboard
Developer Debugging
Key Points
- Step-level tracing
- Each reasoning step, tool call, and its result needs to be captured, not just the final model response.
- Cost and latency per step
- Tracing lets a team see which step of an agent’s loop is slow or expensive, not just the total.
- Correlation across a run
- All steps of one agent run need a shared trace ID so they can be viewed together, in order.
Interview Question
An agent produces a wrong answer in production. How would you debug it?
I’d start from the full trace of that run — every reasoning step, tool call, and intermediate result, in order — rather than just the final output, since the wrong answer could stem from a bad tool choice, wrong tool arguments, or misinterpreting a tool result several steps earlier. Without step-level tracing, that root cause is effectively invisible.
Explain It in 30 Seconds
Agent observability traces every step of an agent’s loop — decisions, tool calls, results — under one correlated run ID, since debugging a wrong outcome requires seeing the full path that led there, not just the final answer.
Real-World Stack
Technologies commonly used to implement this in production.