The Claims Assistant That Did Not Need a Swarm
A leadership team replaces an impressive but untestable multi-agent proposal with a staged architecture that earns complexity from evidence.
Scenariet
Teaching composite — not a claim about a named organisation or measured outcome. A claims organisation wants an assistant to prepare a case summary from policy documents, adjuster notes and photographs before a human handler decides the next step. The proposal has a planner, five specialist agents, an autonomous web researcher, a reviewer and a supervisor. It looks sophisticated, but no one can say which agent owns a wrong recommendation, which tools may access claimant material, or how a failed run resumes.
The claims officer reframes the design. The first release needs to retrieve approved internal evidence, extract a fixed summary, identify missing evidence and prepare—not make—the handler's recommendation. A single retrieval-grounded agent with a structured output may meet that need. A planner, specialist workers or browser automation may be added later only if representative evaluation shows a specific limitation that the simpler system cannot meet.
The team records the deterministic controls: case access, permitted sources, validation, the action boundary and a human approval before any customer communication. It defines an evaluation set including ordinary claims, conflicting notes, missing documents and requests outside the assistant's authority. The result is not less ambitious; it is a design the business can test, explain and safely improve.
Sådan kommer AI ind i arbejdsgangen
Frame the decision
MenneskeThe claims officer defines the first work product, accountable handler and unacceptable failure.
Choose the minimum pattern
Menneske + AIThe team tests whether one retrieval-grounded agent can produce the structured summary before adding a planner or worker team.
Set deterministic controls
MenneskeIdentity, case access, source eligibility, validation and the action boundary remain code-owned.
Run representative evaluation
Menneske + AIHandlers test normal, conflicting, incomplete and out-of-scope claims; the system records quality, latency, cost and failures.
Approve the next complexity
MenneskeThe owner decides whether evidence justifies a new tool, loop, specialist or durable graph workflow.
Spørg om workflowet
ADA, taskforcens næstkommanderende, forklarer præcis, hvordan menneske og AI deler arbejdet — spørg om hvad som helst.
Taskforcen: menneske + agenter
Teamleder — godkender hvert resultat
Teamleder — godkender hvert resultat
MAESTRO
Delivery & Engagement Manager
PRISM
Readiness & Assessment Agent
SENTINEL
Governance Agent
Kuni
AI Learning Companion
NOVA
Intelligence & Industry Research Agent
Taylor
Orchestrator Agent
FORGE
Engineering Agent
ORION
Infrastructure Agent
Examiner
Assessment Agent
Agenter foreslår og udfører. Den menneskelige leder godkender altid det endelige resultat.
Resultater
| Metrik | Før | Efter |
|---|---|---|
| Architecture choice | A labelled but untestable swarm | A minimum design with an explicit upgrade trigger |
| Control ownership | Implied by prompts | Code-owned permissions, validation and approval |
| Learning | Demo impressions | Representative quality, recovery, cost and safety evidence |
Vigtigste pointer
- Start with the smallest pattern that can meet the work product and quality bar.
- A model may interpret evidence; code must control access, validation and irreversible state changes.
- Each added loop, tool or specialist needs a measured failure it solves and a bounded recovery path.
- Human approval is an operational control only when rejection, modification and resume are all explicit.
Relaterede koncepter
Gå dybere
Klar til at bringe AI ind i jeres organisation?
Tal med os om en guidet adoptionsvej for jeres team — fra første use case til produktion.
Spørg om dette koncept