Multi-Agent System: Orchestrated AI Pipeline for Document Processing
A multi-agent AI system where specialized agents collaborate to extract, validate, and route data from thousands of documents with human-in-the-loop oversight.
Client: Confidential Insurance Company
System architecture diagram showing 4 agent nodes (Extractor, Validator, Router, Supervisor) connected by arrows, with a human review queue on the side. Below, a live processing feed shows documents moving through stages.
The Challenge
The insurance company received 3,000 claim documents per month: medical bills, repair estimates, police reports, and supporting evidence. Each claim required a human to read the document, extract key data (policy number, claim amount, incident date, claimant info), validate it against policy terms, and route it to the right adjuster based on claim type and amount. This process took 45 minutes per claim and required 8 full-time processors. Errors were common (12% of claims had data entry mistakes), and routing delays added 2-3 days to claim resolution. They had tried OCR with templates, but insurance documents are too varied in format for template-based extraction.
Our Approach
We designed a 4-agent pipeline where each agent specializes in one task. Agent 1 (Extractor) uses Claude's vision capabilities to read any document format (PDF, scanned image, handwritten notes) and extract structured data: policy number, claim amount, dates, parties involved, and claim category. Agent 2 (Validator) checks extracted data against policy rules: Is the policy active? Does the claim amount exceed the deductible? Is the incident date within the coverage period? Agent 3 (Router) assigns the validated claim to the right adjuster based on claim type, amount threshold, and adjuster workload. Agent 4 (Supervisor) handles exceptions: documents the Extractor can't parse, validation failures, and routing conflicts. The Supervisor can request human review, merge duplicate claims, and flag suspicious patterns. All agents communicate through a shared event bus (BullMQ), and every decision is logged with reasoning for audit compliance. A human-in-the-loop dashboard shows the processing queue, allows overriding any agent decision, and provides feedback that improves agent accuracy over time.
What We Built
Delivery Timeline
Day 1-4: Extraction Agent
Claude vision document parsing, structured output, handling PDFs/scans/handwriting, confidence scoring.
Day 5-8: Validation Agent
Policy rule engine, coverage checks, deductible validation, date range verification.
Day 9-12: Router + Supervisor
Routing logic, adjuster workload balancing, exception handling, duplicate detection.
Day 13-16: Orchestration
BullMQ pipeline, agent communication, retry logic, failure recovery.
Day 17-19: Dashboard
Processing feed, override interface, feedback loop, audit trail viewer.
Day 20-21: Launch
Production deployment, historical claim backfill test, processor training.
Tech Stack
Architecture
frontend
Next.js admin dashboard with real-time processing feed.
backend
Hono on Railway with BullMQ event bus for agent communication.
auth
Azure AD SSO with role-based access.
data
PostgreSQL for claims and decisions. S3 for document storage.
ai
Claude 3.5 Sonnet (vision) for extraction. Claude for validation, routing, and supervision.
Security
encryption
All documents and PII encrypted at rest (AES-256) and in transit.
compliance
Insurance regulatory compliance: full audit trail, decision reasoning, human override.
rbac
Processor, Adjuster, Supervisor, and Admin roles with claim-level access control.
audit
Every agent decision logged with confidence, reasoning, and data sources.
The Results
“We went from 8 people spending 45 minutes per claim to 2 people overseeing an AI pipeline that processes claims in 3 minutes. The error rate dropped from 12% to under 2%. This is the future of claims processing.”
Key Takeaways
Multi-agent systems outperform single-agent approaches for complex workflows. Each agent can be optimized, tested, and improved independently.
The Supervisor agent is the most important agent. It handles the 15% of cases that don't fit the happy path. Without it, the system fails on edge cases.
Human-in-the-loop is not optional for regulated industries. The dashboard isn't just for monitoring, it's a compliance requirement. Every AI decision must be auditable and overridable.
Deliverables
FAQ
Frequently Asked Questions
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