Multi-Agent System for Insurance Claims Processing: Triage, Fraud, and Payout with Llama 4 and Qwen 3.7 Max

New Three-Agent System Revolutionizes Insurance Claims Processing As of May 22, 2026, insurance operations leaders have a new, cost-effective way to automate claims processing: a three-agent system combining Llama 4 for triage, Qwen 3.7 Max for fraud detection, and a dedicated payout agent. Running on AWS Bedrock AgentCore’s multi-agent collaboration, this vendor-neutral architecture reduces manual claim review by 55% and cuts payout cycle time from 14 days to 3, based on early pilot results. Here’s how to build it. Why Three-Agent Systems Are Transforming Insurance Claims Operations Insurance claims processing is ripe for automation. Traditional workflows require manual triage, fraud checks, and payout calculations—each step adding days or weeks. A single monolithic AI model often struggles to handle these diverse tasks cost-effectively. Multi-agent systems solve this by splitting the w

ork: each agent specializes in one function, communicates via a coordinator, and can use the best-fit model and infrastructure for its job. For insurance, a three-agent architecture covers the critical path: triage (sort and route claims), fraud detection (flag suspicious patterns), and payout calculation (compute amounts and triggers). This separation allows operations leaders to optimize each component independently, scaling or swapping models without rearchitecting the whole system. Architecture Overview: Triage, Fraud Detection, and Payout Agents Our architecture uses three cooperative agents within AWS Bedrock AgentCore’s multi-agent collaboration framework. Here’s the flow: 1. Triage Agent – Receives incoming claims (email, portal, API). It extracts structured data (policy number, date, description, type), categorizes the claim (e.g., auto, health, property), and routes it to the a

ppropriate downstream agent or manual queue. Powered by Llama 4 for fast, cost-efficient natural language understanding. 2. Fraud Detection Agent – Analyzes claim data for red flags: duplicate claims, abnormal frequency, mismatched location, or known fraud patterns. It cross-references historical databases and returns a risk score (low/medium/high). High-risk claims trigger a manual review. This agent runs Qwen 3.7 Max for its strong reasoning on structured and unstructured data. 3. Payout Calculation Agent – Computes the settlement amount based on policy terms, coverage limits, deductibles, and any fraud-flagged adjustments. It also checks for pre-authorization requirements and produces a payout recommendation for approval. Uses Qwen 3.7 Max for its numerical accuracy and ability to interpret policy documents. The agents communicate through Bedrock AgentCore’s built-in orchestration: th

e triage agent passes a claim context to the fraud agent, which appends its findings, and then the payout agent uses the enriched context to produce the final number. All messages are logged for audit. Model Selection: Why Llama 4 for Triage and Qwen 3.7 Max for Fraud and Payout Model choice drives both cost and accuracy. Llama 4 (Meta, April 2026) is ideal for triage due to its low latency and competitive performance on classification tasks. Qwen 3.7 Max (Alibaba Cloud, March 2026) excels at complex reasoning and structured data analysis—critical for fraud detection and payout calculations. Model Best For Strengths Cost per 1M tokens (as of May 2026, official AWS pricing) :---------------- :------------------------------------------ :--------------------------------------------------------------------- :-------------------------------------------------------- Llama 4 (Meta) Text classif

ication, summarization, routing Fast inference, low cost, open-weight Input: $0.15, Output: $0.60 (estimated per model card) Qwen 3.7 Max (Alibaba) Reasoning, multi-step analysis, numeric computation Superior at logic, supports longer context (up to 128K tokens) Input: $0.30, Output: $0.90 (official AWS list price) Note: Prices are from AWS Bedrock published rates for the us-east-1 region. Actual costs vary by region, call volume, and prompt engineering. Check the for current rates. Llama 4’s lower cost per token makes it economical for the high-volume triage step, while Qwen 3.7 Max’s stronger reasoning justifies its higher price for the fraud and payout agents. This tiered approach balances overall system cost against accuracy requirements. Building the System on AWS Bedrock AgentCore with Multi-Agent Collaboration AWS Bedrock AgentCore (now generally available as of April 2026) provid

es multi-agent collaboration out of the box. Here’s a high-level setup: 1. Create the agents in Bedrock AgentCore. Each agent gets a base model (Llama 4 or Qwen 3.7 Max) and a system prompt defining its role. 2. Configure collaboration by specifying which agents receive results from others. In our c