How a Three-Agent System on AWS Bedrock Cut Claims Processing Time by 35%

Why a Three-Agent Architecture for Insurance Claims? As of May 23, 2026, insurance carriers face mounting pressure to process claims faster while combating sophisticated fraud schemes. Traditional linear workflows—often reliant on manual triage, rule-based fraud checks, and human validation—struggle to keep pace with claim volume and evolving fraud tactics. Multi-agent claims processing offers a transformative alternative: a system of specialized AI agents that collaborate to handle subtasks independently, then hand off results to the next agent. A regional insurer recently piloted a three-agent system on AWS Bedrock, processing 10,000 claims over three months. The architecture divided work into three distinct roles: - Llama 5 for claim triage (classifying claim type, severity, and routing) - Qwen 3.8 Max for fraud pattern analysis (detecting anomalies, cross-referencing databases) - A f

ine-tuned claims validation agent for flagging inconsistencies and recommending adjudication The results: a 35% reduction in average processing time (from 12 days to under 8) and a 20% improvement in fraud detection accuracy (from 72% to 86.4% true positive rate). This article walks through the architecture decisions that made it work—model selection, handoff design, and cost-per-claim benchmarks—so operations leaders can evaluate multi-agent systems for their own claims workflows. Model Selection: Llama 5 for Triage, Qwen 3.8 Max for Fraud Choosing the right model for each agent is critical. The pilot’s triage agent uses Meta’s Llama 5 (released early 2026 on AWS Bedrock), while the fraud analysis agent runs Qwen 3.8 Max from Alibaba Cloud. Why these two? Llama 5 for Triage Triage requires fast, accurate classification of claim attributes: claim type (auto, property, health), severity (

low/medium/high), and initial routing. Llama 5 excels at structured text classification with low latency. Key specs: - 70B parameters, optimized for instruction following - Strong performance on document summarization and entity extraction (claims descriptions, policy numbers) - Per-token cost on Bedrock: $0.0035 per 1K input tokens, $0.005 per 1K output tokens Qwen 3.8 Max for Fraud Detection Fraud detection demands multimodal reasoning—analyzing claim narratives, images of damage, historical claim databases, and external watchlists. Qwen 3.8 Max (model card: ) offers a 128K context window and native vision support, enabling it to process full claim packets in one pass. It also supports function calling to query SQL databases and third-party fraud APIs. Cost: $0.007 per 1K input tokens, $0.012 per 1K output tokens. Fine-Tuned Validation Agent The third agent is a fine-tuned version of a

smaller model (Llama 5 8B) trained on 5,000 labeled claim approvals and rejections from the insurer’s historical data. Its role: review outputs from triage and fraud agents, cross-check policy coverage, and generate a final recommendation with confidence scores. Fine-tuning cost approximately $2,500 in compute credits on Bedrock; inference cost is $0.001 per 1K tokens. Selection criteria summary: - Latency requirements (triage: seconds; fraud: minutes) - Context length needed (fraud needs large context) - Multimodal capability (only fraud agent needs image support) - Cost sensitivity (triage agent runs on highest volume, so cheaper per-token is preferred) Designing Agent Handoffs for Optimal Workflow A multi-agent system is only as strong as its handoff protocol. The pilot used AWS Bedrock’s multi-agent collaboration feature , which provides a managed orchestration layer with message qu

eues, error handling, and state management. The handoff sequence: 1. Triage Agent (Llama 5) receives a new claim submission and outputs a JSON payload: . 2. Orchestrator (Bedrock AgentCore) reads the routing field and forwards the full claim packet (text + images) to the Fraud Agent (Qwen 3.8 Max) . 3. Fraud Agent runs pattern analysis—checks historical claims for similar damage patterns, queries external fraud databases via API, and scores the claim on a 0–100 fraud likelihood scale. Output: . 4. Validation Agent receives triage summary + fraud score + policy data and applies fine-tuned rules. It either approves (with conditions), flags for manual review, or rejects—with a detailed explanation. 5. Orchestrator logs all outputs to an S3 bucket for auditing and triggers downstream systems (payment, adjuster scheduling). Error handling: Each agent has a timeout (15 seconds for triage, 60 s

econds for fraud) and retry logic (2 retries). If an agent fails, the orchestrator marks the claim for manual intervention. The pilot achieved 96% uptime across agents. Cost-Per-Claim Benchmarks from a 10,000-Claim Pilot Accurate cost estimation is essential for building a business case. Below are p