Multi-agent claims triage that carriers can defend

The problem

A first notice of loss arrives as an email with three attachments, a phone transcript, and a photo. The carrier wants to triage it in seconds: coverage analysis, fraud signal, severity estimate, adjuster routing. A monolithic LLM prompt cannot do this well. A pile of disconnected agents cannot defend its decisions. Regulators in most US states require the carrier to explain why a claim was handled the way it was. "The AI decided" is not an answer.

Why the usual approach breaks

Teams chain agents with LangChain or a homegrown orchestrator. Each agent calls the next. The chain breaks when one agent returns a malformed output. Observability is thin. When a claim is handled incorrectly and the postmortem starts, nobody can tell whether the error came from the intake parser, the coverage reasoner, or the routing step. The carrier's model governance team blocks the production launch pending a control story that nobody has time to write.

How Intelligence Fabric closes the gap

Fabric models each agent as an inventoried, versioned service with defined inputs, outputs, and policy bindings. Agents compose through typed interfaces rather than brittle string passing. The orchestrator emits a structured execution trace for every claim: which agent ran, which version, which tools it called, which documents it retrieved, which policies were checked, which decision it returned.

When the claim goes to a human adjuster, the trace comes with it as a reviewable evidence packet. When the adjuster overrides a recommendation, the override is captured against the same claim ID and flows back into the evaluation set. The agents improve because the feedback loop is real, not theoretical.

Implementation pattern

Intake parser classifies the submission type, extracts entities, and routes to the coverage agent. Coverage agent retrieves the policy, cross-references exclusions and endorsements, and returns a structured coverage opinion with citations to the policy document. Fraud agent scores the claim against historical patterns and network-graph signals. Severity agent estimates reserve requirement. Routing agent picks the adjuster queue based on line of business, severity, and workload. Every agent output includes a confidence score and a deferral path: if confidence is below threshold, the claim is routed to human review rather than auto-processed.

Next step

An architecture review takes your current triage pipeline, identifies the two decisions most at risk of a regulatory or reputational failure, and designs a Fabric-based orchestration that keeps those decisions explainable.

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