Why Is Our AI Agent Making Mistakes?
Repeated mistakes need to be traced to the task, source information, tools and approval rules before changing the model.
Separate the failure types
Record whether the agent misunderstood the request, retrieved the wrong information, used stale data, selected an inappropriate tool or executed the correct tool with incorrect inputs. A fluent answer can still lead to a wrong action.
Build a small incident evidence set
Keep the request, source version, retrieved records, tool arguments, result and reviewer correction for each incident. Remove personal data that is not needed for diagnosis. Compare failed runs with successful runs of the same task.
Contain the action before retesting
For actions that can affect customers, payments or records, narrow permissions or require approval while the cause is investigated. Test a correction on normal cases, missing information and deliberate exceptions before returning to the previous level of autonomy.
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