Production AI agents need engineered recovery loops
A production AI agent needs more than the ability to finish a task once. Its surrounding loop should observe each run, find the first point where the process went wrong, and choose whether to retry, resume, roll back, or send the problem to a person.
Every failure should also produce something reusable, such as a trace, root-cause label, eval case, change, recovery rule, or point. Those lessons can then update policies or the so the next run is safer.
This proposed discipline, called “,” combines , testing, workflow coordination, system , and product . It may be a useful new label, although it could also be existing practices applied to AI agents.
Key points
- Design the response to failure, not just the agent’s first attempt.
- Find where a run first went wrong before choosing to retry, resume, roll back, or ask a person for help.
- Turn failures into reusable traces, eval cases, changes, and recovery rules.
- Feed lessons from failed runs into policies and the for safer future runs.
- Track retries and token use to check whether these changes actually reduce costs.