How do I monitor an AI agent that handles claims intake so it doesn't make up answers?

Test the agent before it touches a real claim, then supervise it continuously once it's live. Build an answer key from claims your adjusters have already worked, decide ahead of time which mistakes are never acceptable, and run the agent against that key until the results are statistically meaningful. In production, check every extracted fact against its source document, watch how the agent's behavior shifts across many claims, and alert your team within the first handful of bad outputs.

Why do claims intake AI agents make things up?

A language model writes the most likely answer, and a confident wrong date reads exactly like a right one. Claims intake is high volume and document heavy, with first notice of loss notes, photos, policy files and demand packages that can run hundreds of pages. Grounding the agent in your source documents cuts down on invented answers, but it doesn't remove them, so you still need a way to catch the ones that slip through.

How do I test a claims AI agent before it touches a real claim?

Start with an answer key. Pull a set of past claims your adjusters already worked and write down the facts the agent has to get right: date of loss, policy and coverage, parties, amounts, injuries, and any deadline in the file.

Then decide which misses are critical. Some errors should have zero tolerance, like a missed demand deadline, a wrong date of loss or an amount the documents don't support. Others, like how a summary is worded, can have a tolerance. Write the pass bar down before you run a single test.

Test consistency before accuracy. Run the same claim through the agent several times. If the key facts change from run to run, there's no point measuring accuracy yet.

Then test accuracy against the answer key, and grow the test until the result means something. About 385 test cases per use case gets you to 95% confidence within plus or minus 5 points. Include the hard cases on purpose: long demand packages, pages out of order, handwritten notes, and statements that contradict each other.

What should I monitor once the agent is live?

• Source checks: every fact the agent extracts should trace back to the document it came from. Flag anything with no source.
• Critical-miss alerts: the zero-tolerance errors from your test plan should alert someone right away.
• Drift across claims: watch the mix of outputs, not only single answers. If the share of claims the agent routes to a fast track suddenly climbs, or a field starts coming back blank more often, find out why before it becomes a pattern.
• Human review samples: send a steady sample of outputs to adjusters. Their corrections become new entries in the answer key.
• Re-testing before changes: run the same answer key before any model, prompt or vendor update goes live.
• Logs and an audit trail: keep a record of inputs, outputs and reviews that you can show in a market conduct exam.

Are confidence scores enough to catch hallucinations?

Confidence scores help with routing, because low-confidence answers can go to a person. They aren't proof of accuracy, since a model can be confidently wrong. Treat them as one signal next to source checks, critical-miss alerts and an answer key you trust.

How fast should we catch a problem?

Within the first handful of bad outputs. A quarterly or annual review lets thousands of claims go through before anyone notices a change. Set alert sensitivity by risk: a summary an adjuster always reads can tolerate more drift than an agent that routes claims or suggests reserves with no one checking.

Who should own monitoring a claims AI agent?

The claims leader owns the answer key and the list of critical misses, because they know what wrong looks like. The IT or AI team owns the test runs, alerts and re-testing. Compliance owns the audit trail. On a small team one person may wear two of those hats, which is fine as long as each job has a name next to it.

Can Swept monitor our claims intake agent?

Yes. Swept evaluates claims agents on your own claims before go-live and supervises them continuously after, on your data and in your environment. It works whether your team built the agent or it came from a vendor, as long as there's a way to send inputs and capture outputs. If you want to run a first pass yourself, start with our AI evaluation scorecard.

Talk to us about a claims agent evaluation