When Your AI Can Log Into the Claims System: Supervising Agents That Act, Not Just Draft

AI in credit hire used to draft. This week it started to act. New assistants can open a browser, log into a live system and take steps on their own. Here is what changes for handlers when the tool stops writing letters and starts working the file, and the three controls to put in place before it does.

For the last two years, AI on a credit hire desk has done one job. It drafts. It writes the BHR challenge, pulls the case law, summarises the file. A person then reads it, checks it, and sends it. The human is the gate, and the AI never touches anything live.

That changed this week. The newest assistants can open a browser, log into a system on your behalf, and take actions inside it. Ask one to check a comparator rate and it can go and check. Ask one to update a case note and it can go and update. The industry name for this is an agent, and the shift is simple to state. The tool has moved from writing about the work to doing the work.

You can see the same shift landing in insurance already. This week Cytora's chief executive said its Zurich deployment shows agentic AI is now past the pilot stage, running live across underwriting in several countries. Underwriting is moving first because it is structured and high volume. Claims, credit hire included, is the obvious next stop. So this is worth getting ahead of now, before an acting agent turns up on your desk by default rather than by decision.

The difference between a draft and an action

A draft is safe by design. If the AI writes a BHR challenge that quotes the wrong comparator, you catch it when you read it, and nothing has happened yet. The mistake lives in a document, not on the file.

An action is different. If an agent logs into the case management system and applies a hire period reduction, or sends a note to the third party insurer, the mistake is not sitting in a draft waiting for a read. It has already happened. Undoing it means spotting it first, then working out what the agent did, then reversing it. On a credit hire file, where the paper trail is the case, an unsupervised action you cannot reconstruct is a real problem.

That is the whole supervision question in one line. When the AI only drafts, the human is the gate before anything happens. When the AI acts, the gate has to be built in, because by the time you are reading the output the thing is already done.

An agent with a login is staff

The most useful way to think about an acting agent is not as a piece of software. It is as a new joiner.

A new handler on their first week does not get the keys to everything. They get a login with defined access. Someone signs off what they can touch. And everything they do is logged, so if a file goes wrong you can see who did what and when. Nobody finds that controversial. It is just how you run a claims operation.

An acting agent needs exactly the same three things, for exactly the same reasons. The fact that it is fast and tireless does not change the governance. If anything it raises the stakes, because it can take a hundred actions in the time a person takes one. Speed is the benefit. It is also the reason the controls have to be right before you switch it on, not after.

The three controls to put in place first

Before an agent touches a single live file, three things need to be defined. None of them is technical. All of them are operational decisions a claims manager can make.

One. The access boundary. Write down which systems the agent may reach and which it may not. Reading rate data, fine. Drafting into a working document, fine. Posting to the third party insurer or changing a hire period on the live record, that is a different level of access and should be a deliberate choice, not a default. Start narrow. Widen only when you have watched it work.

Two. The named approver. One person owns the decision to give an agent a new permission. When someone wants the agent to start doing a new thing on live files, it goes through that person. This is the same principle as not letting a new starter grant themselves system access. If everyone can extend what the agent does, nobody is actually in control of it.

Three. The action log. Every step the agent takes has to be recorded somewhere you can pull it. What it did, on which file, when, and off the back of what instruction. On a credit hire file this is not optional housekeeping. If a defendant insurer challenges how a decision on the file was reached, "the system did it and we cannot show you the steps" is not an answer you want to be giving. If you can show the log, you are fine.

What this does not change

The evidence standard on the file has not moved an inch. A hire period still has to be justified on need for hire, actual repair timeline, vehicle group and mitigation. Whether a human typed the reduction or an agent applied it, the reasoning has to hold up in exactly the same way. An agent does not lower the bar. It just applies whatever the bar is, faster.

And the case law is untouched. Burdis v Livsey, Bent v Highways, Clark v Ardington apply the same as they did last month. Agentic AI is a change in how the work gets done, not a change in what makes a credit hire claim recoverable.

The point of naming all this now is not to slow anyone down. It is so that when the acting agent does arrive on the desk, and it will, you are putting it to work inside a structure you designed, rather than discovering the structure you needed after something has already gone onto a file.

Where CreditHire Assist fits

CHA is built on the drafting side of this line, on purpose. Every argument the platform produces is grounded in verified evidence: the customer's actual vehicle group, the actual comparator rates, the case law that applies at the time of the hire. There is a hard non-hallucination rule. The system does not invent rates, cases or facts, and nothing reaches a letter that the handler cannot trace back to a source.

That design matters more, not less, as agents start to act. An acting agent is only as safe as the material it works from. If the underlying arguments are grounded and auditable, an agent built on top of them has something reliable to act on, and a log you can actually stand behind. If the underlying material is a black box, speeding it up just gets you to the wrong place faster.

The 3Rs test still decides where any of this belongs. Credit hire correspondence is repetitive, rules-based and resource-intensive, which is exactly why AI earns its place on the desk. Acting agents will earn their place too, on the tasks where the boundary is clear and the log is clean. The job this year is to set those boundaries early rather than inherit someone else's.

FAQ

CreditHire Assist drafts BHR challenges, TPI rebuttals and case law arguments for handlers in minutes, grounded in verified evidence with a full audit trail. See it in action.

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