croissantWhat to monitor after launching an AI agent
Track failures, unanswered work, stale tasks, and useful outcomes so an agent remains an owned business process.
An agent that worked in a demonstration can still fail quietly in production. Inputs change, credentials expire, and external systems become unavailable. Monitoring should tell a responsible person that useful work has stopped or become unreliable, rather than merely showing that a process is running.
Watch the business queue
Look for work without an owner, requests waiting beyond the expected operating window, repeated failures, and tasks that reopen without progress. A healthy process can be idle; an unhealthy one can produce many log messages. Queue state helps distinguish the two.
Track dependencies and permission failures
Separate an unavailable tool from a rejected action or missing access. The recovery path differs. Do not respond to an authorization error by giving the agent broader access automatically. Record the affected workflow and let its owner decide whether the original permission is still appropriate.
Example: credentials stop working overnight
An inbox workflow might continue running while every attempt to read new mail fails. A process-alive check would miss the problem. A useful monitor notices the absence of successful intake, records the authentication failure without exposing credentials, and notifies the owner once with an actionable status. After access is restored, verify that the cursor or checkpoint resumes from the correct point and that messages are neither skipped nor processed twice.
Make alerts actionable
An alert should identify the affected work, the observed failure, and the next safe action. Deduplicate repeated alerts for the same incident. Define who responds and how the workflow resumes after a repair so the team does not discover a backlog only when a customer complains.
A reality check before you hit run
Anthropic describes evaluations as complementary to production monitoring. Keep a repeatable test set as well as runtime alerts. [1]
NIST treats evaluation and use as parts of the same AI lifecycle. Keep ownership and review active after deployment. [2]
Implementation checklist
- Monitor queue age and useful completion.
- Separate dependency failures from permission problems.
- Assign an owner to each alert.
- Verify recovery on the original failed work.
The sources behind the sauce.
Primary research and official documentation behind the cited points. The workflow examples and checklists are Croissant’s own guidance.