Agency17 June 20263 min read
AI agent observability: knowing what your agents did, and why
Your agents ran overnight. This morning there are merged changes, a token bill, and something that looks off. Can you reconstruct what they did and why? That question is what agent observability answers, and your existing dashboards don't.
The short answer
Agent observability is being able to answer, after the fact, what an AI agent did and why. It records the decisions an agent made, every action it took (tool calls, edits, commands, and their results), what each step cost, and whether the work passed your gates. App monitoring doesn't cover this: it watches a service's health, not a non-deterministic actor's behavior. Without agent observability you're running agents on faith, and the first thing you can't explain ends the rollout.
Short version: an agent ran while you slept. This morning there are merged changes, a token bill bigger than you expected, and a test that's suddenly red. What did it actually do, and why? If you can't answer that from a record, you're running agents on faith. Agent observability is the ability to reconstruct an agent's behavior after the fact, the decisions, the actions, the cost, the outcome, and it's a different thing from the monitoring you already have.