AI Agent Observability Questions Before Launch: Observability is not a dashboard screenshot. For AI agents, observability means the team can reconstruct what the agent saw, why it acted, which tools it called, and what it cost.
Can we replay a bad run?
If a user reports a bad answer or unsafe action, the team should be able to see the prompt, retrieved context, tool calls, tool arguments, model output, approval decision, and final user-facing response.
Can we separate model failure from system failure?
A failed run may come from retrieval, permissions, tool timeout, stale data, policy conflict, or a weak prompt. If all failures look like “the model was wrong,” the system is not observable enough.
Do we track denied tool calls?
Denied calls are useful signals. They show where the agent wanted to act but policy, approval, or validation stopped it. That is often where future incidents begin.
Do we know cost per completed task?
Token cost alone is incomplete. Track cost per completed task, cost per escalation, retries, tool latency, and whether expensive calls actually improved outcomes.
Who gets alerted?
Alerts should map to owners. Cost spike, tool error, unsafe-action attempt, high escalation rate, and retrieval failure should not all go to the same generic inbox.
I would use this for an AI agent launch review: AI Agent Observability Questions Before Launch. It turns risk into concrete checks instead of abstract advice.Related resources
Next step
If these issues already affect real users or customer data, run the AI Agent Readiness Self-Assessment first, then turn blockers into a launch checklist.