Before an AI agent takes action in insurance
Reading a document and changing a business record carry different risks. Put evidence, authority and review into the workflow.
Define what an AI agent may read, recommend and change before giving it access to a live insurance workflow.
Separate assistance from authority
An AI assistant that summarizes a submission is helping someone interpret information. An agent that changes a policy record or sends a payment instruction is taking an operational action. Those activities need different controls, even when they appear in the same interface.
Start with a precise task. Identify the inputs, the proposed output and the person accountable for the result. “Help with claims” is too broad. “Prepare a document summary for an authorised claims handler to review” is a task that can be tested and governed.
Keep the evidence close
A useful result should be easy to check against its source. For document extraction, reviewers should be able to find the relevant passage and understand whether the information was stated directly or inferred. Missing and conflicting details should remain visible.
Well-written output can still be wrong. A review process should test factual accuracy, omitted conditions and whether the result concerns the correct client, policy and date. Fluency is not evidence of a sound insurance decision.
Set limits on system actions
Give an agent only the access needed for its assigned task. Distinguish permission to read a record from permission to update it. Define which changes need a person’s approval and which conditions require the workflow to stop.
The surrounding software should validate required fields and enforce business rules before committing a change. It should also prevent a retry from unintentionally creating a duplicate action. These are operating controls, not responsibilities to leave to a generated instruction alone.
Make review and recovery part of delivery
Keep a record of the input, proposed action, approval and final system response. Teams need enough detail to investigate an error, correct the record and understand whether the same issue affects other cases. Decide who monitors that process before launch.
Test on representative cases, including incomplete documents and unusual wording. Track corrections and escalations as well as time saved. Expand authority only when the evidence supports it. These are implementation principles for insurance AI; the controls and capabilities of any particular Ebix product should be confirmed for the intended use case.