Building a Multi-Agent System: Lessons from Our Document Processing Pipeline

When we started processing thousands of legal documents daily, a single AI agent couldn't handle the complexity. Different document types required specialized processing, and we needed reliable error handling and recovery. Here's how we built a multi-agent system that solved these problems.

The Problem: Complex Document Workflows

Our initial single-agent approach failed for several reasons:

Specialization needs: Contracts required different analysis than invoices or legal briefs
Processing bottlenecks: One slow document blocked the entire queue
Error propagation: A failure in one step killed the entire workflow
Scaling issues: We couldn't easily add new document types

Our Multi-Agent Architecture

We designed a system with four specialized agent types:

1. Document Classifier Agent

Role: Determines document type and routes to appropriate specialists

Implementation:

Uses a fine-tuned model trained on document headers, layouts, and key phrases
Returns confidence scores for each document type
Handles edge cases by routing to human reviewers when confidence < 0.8

class DocumentClassifier:
    def classify(self, document):
        features = self.extract_features(document)
        prediction = self.model.predict(features)
        
        if prediction.confidence < 0.8:
            return self.route_to_human_review(document)
        
        return prediction.document_type

2. Specialist Processing Agents

Contract Agent: Extracts parties, terms, dates, and obligations Invoice Agent: Pulls vendor info, line items, totals, and tax details
Legal Brief Agent: Identifies arguments, citations, and case references

Each specialist uses domain-specific prompts and validation rules:

class ContractAgent:
    def process(self, document):
        extracted_data = self.llm.extract(
            document, 
            schema=self.contract_schema,
            examples=self.few_shot_examples
        )
        
        # Validate extracted data
        if not self.validate_contract_data(extracted_data):
            return self.request_human_review(document, extracted_data)
            
        return extracted_data

3. Quality Assurance Agent

Role: Reviews outputs from specialist agents for consistency and accuracy

Checks performed:

Cross-references extracted dates for logical consistency
Validates monetary amounts and calculations
Flags unusual patterns for human review
Ensures required fields are populated

4. Coordinator Agent

Role: Orchestrates the entire workflow and handles inter-agent communication

Responsibilities:

Manages the processing queue
Coordinates handoffs between agents
Handles retries and error recovery
Tracks processing status and generates reports

Agent Coordination Patterns

Message Passing

We use a simple message bus pattern for agent communication:

class MessageBus:
    def __init__(self):
        self.queues = defaultdict(list)
        
    def send_message(self, recipient, message):
        self.queues[recipient].append(message)
        
    def get_messages(self, agent_id):
        messages = self.queues[agent_id]
        self.queues[agent_id] = []
        return messages

Workflow States

Each document moves through defined states:

RECEIVED → Document enters system
CLASSIFIED → Document type determined
PROCESSING → Specialist agent working
QA_REVIEW → Quality assurance checks
COMPLETED → Successfully processed
HUMAN_REVIEW → Requires manual intervention
FAILED → Unrecoverable error

Error Handling Strategy

Graceful degradation: If a specialist agent fails, the system:

Retries with different parameters
Routes to a generalist backup agent
Escalates to human review with context

Circuit breaker pattern: Temporarily disable failing agents to prevent cascade failures

Real-World Example: Processing a Legal Contract

Here's how our system processes a 15-page software license agreement:

Document Classifier analyzes the header and identifies it as a software contract (confidence: 0.94)
Contract Agent extracts:
- Parties: "TechCorp Inc." and "Client Solutions LLC"
- License type: "Perpetual, non-exclusive"
- Payment terms: "$50,000 upfront, $10,000 annual maintenance"
- Termination clauses: 3 identified conditions
QA Agent validates:
- Confirms payment amounts are consistent throughout document
- Flags potential issue: Termination notice period differs in two clauses (30 days vs 60 days)
- Routes to human reviewer for clarification
Coordinator updates status and notifies relevant stakeholders

Total processing time: 45 seconds (vs 2-3 hours for manual review)

Performance Results

After six months of operation:

Accuracy: 94% for fully automated processing
Speed: 40x faster than manual processing
Scalability: Added two new document types in one week
Reliability: 99.2% uptime with graceful error handling

Key Implementation Lessons

Start Simple

We began with just two agents (classifier and processor) before adding specialists. This helped us understand coordination complexity without overwhelming the initial build.

Design for Observability

Every agent logs its decisions and confidence levels. This visibility proved crucial for debugging and improving performance:

class Agent:
    def process(self, task):
        self.logger.info(f"Starting {task.type} with confidence {self.confidence}")
        result = self.execute(task)
        self.logger.info(f"Completed {task.type}: {result.summary}")
        return result

Human-in-the-Loop is Essential

Our best agents still make mistakes. Building smooth handoffs to human reviewers from day one saved us countless hours of manual cleanup.

Agent Specialization vs Generalization

Highly specialized agents outperformed generalist agents by 15-20% accuracy, but required more maintenance. We found the sweet spot at 3-4 specialist types covering 80% of our document volume.

Next Steps

We're currently exploring:

Self-improving agents that learn from human corrections
Dynamic agent spawning for handling document volume spikes
Cross-agent knowledge sharing to improve specialist performance

The multi-agent approach transformed our document processing from a bottleneck into a competitive advantage. The key was starting with clear agent responsibilities and building robust coordination from the beginning.