What Happens When an AI Agent Fails?
A useful recovery plan identifies who can stop an agent, how unfinished work is handled and how people resume the process safely.
Define stop conditions
Identify repeated errors, missing approval, unexpected spending and inconsistent system state as possible triggers. Assign responsibility for stopping the workflow and deciding when it may resume.
Reconcile actions already taken
Distinguish requested, attempted and confirmed actions. Check external systems before retrying so a payment, message or record change is not duplicated. Some actions cannot be undone and need an escalation process.
Rehearse the fallback
Test loss of a tool, unavailable source data and interrupted execution in a controlled environment. Confirm that a person can see pending work, complete it manually and record the resolution. Capture lessons in the operating procedure.
What to prepare for a review
Bring a short workflow description, representative examples, available evidence and the outcome you expected. Start with anonymised examples; agree a secure method before sharing sensitive records.
Get help with this workflow
Explore AI Agent Controls for a scoped independent review.
Tell us where AI is getting stuck
Describe the workflow, what you expected and what is happening instead. We will clarify the scope and information needed for a review.
Discuss your AI workflow