Building a Multi-Agent System: Lessons from Production

Multi-agent systems sound complex, but they're essentially distributed applications where each component (agent) has a specific role and can make autonomous decisions. After building one for document processing, here's what we learned.

Why We Chose a Multi-Agent Architecture

Our challenge was processing mixed document types—PDFs, spreadsheets, images with text—each requiring different extraction methods. A monolithic processor would be brittle and hard to maintain.

Instead, we designed specialized agents:

• Router Agent: Classifies incoming documents
• PDF Agent: Handles text extraction from PDFs
• OCR Agent: Processes images and scanned documents
• Structured Agent: Parses spreadsheets and tables
• Coordinator Agent: Orchestrates the workflow and handles results

Core Architecture Patterns

Message-Based Communication

Agents communicate through a message queue (we used Redis) rather than direct API calls. This provides:

• Decoupling: Agents don't need to know about each other's implementation
• Reliability: Messages persist if an agent is temporarily down
• Scalability: Easy to add more instances of any agent type

# Simple message structure
{
  "task_id": "doc_123",
  "agent_type": "pdf_processor",
  "payload": {
    "document_url": "...",
    "extraction_params": {...}
  },
  "metadata": {
    "timestamp": "2024-01-15T10:30:00Z",
    "retry_count": 0
  }
}

Agent State Management

Each agent maintains minimal state, storing only:

• Current task being processed
• Configuration parameters
• Health status

All persistent data lives in shared storage (database), not within agents.

Coordination Strategies

Workflow Orchestration

We implemented two coordination patterns:

Centralized (Coordinator Agent)

• One agent manages the entire workflow
• Simpler to debug and monitor
• Single point of failure risk

Distributed (Event-Driven)

• Agents subscribe to relevant events and act autonomously
• More resilient but harder to trace execution

We started with centralized and later moved to hybrid approach for critical paths.

Example Workflow

1. Document uploaded → Router Agent classifies it
2. Router publishes classification result
3. Appropriate processor agent picks up the task
4. Processor extracts content and publishes result
5. Coordinator Agent aggregates results and notifies completion

Error Handling and Resilience

Retry Logic

Each message includes retry metadata:

def process_with_retry(message, max_retries=3):
    retry_count = message.get('metadata', {}).get('retry_count', 0)
    
    try:
        return process_message(message)
    except RetryableError as e:
        if retry_count < max_retries:
            # Exponential backoff
            delay = 2 ** retry_count
            schedule_retry(message, delay)
        else:
            send_to_dead_letter_queue(message)

Circuit Breakers

Agents monitor their own health and stop accepting new work when error rates exceed thresholds.

Graceful Degradation

If the OCR agent is down, the system still processes PDFs and spreadsheets, marking image documents for later processing.

Monitoring and Observability

Key Metrics

• Message queue depth per agent type
• Processing time by document type
• Success/failure rates per agent
• Agent health status and resource usage

Distributed Tracing

Each task gets a unique trace ID that follows it through all agents. This makes debugging much easier than trying to correlate separate log files.

Practical Implementation Tips

Start Simple

Begin with fewer agents and split responsibilities as you identify clear boundaries. Our first version had just three agents—router, processor, and coordinator.

Agent Discovery

Use a service registry so agents can find each other without hardcoded endpoints. We used Redis for this too:

def register_agent(agent_id, agent_type, capabilities):
    redis_client.hset(
        f"agents:{agent_type}", 
        agent_id, 
        json.dumps({
            "capabilities": capabilities,
            "last_seen": time.time()
        })
    )

Configuration Management

Centralize configuration but allow agent-level overrides. Use environment variables for deployment-specific settings and a shared config service for business logic parameters.

Testing Strategies

• Unit tests for individual agent logic
• Integration tests for message handling
• End-to-end tests for complete workflows
• Chaos engineering to test failure scenarios

Common Pitfalls

Over-Engineering

Don't create an agent for every small function. Agents should have meaningful, cohesive responsibilities.

Ignoring Network Partitions

Always assume messages can be lost, duplicated, or delivered out of order. Make operations idempotent where possible.

Insufficient Monitoring

Distributed systems are harder to debug. Invest in observability from day one.

Results and Lessons Learned

Our multi-agent system processes 10,000+ documents daily with 99.5% reliability. Key takeaways:

• Message queues are your friend: They provide natural resilience and scalability
• Keep agents focused: Single responsibility makes them easier to test and maintain
• Plan for failures: Network issues, agent crashes, and resource constraints will happen
• Observability is critical: You can't fix what you can't see

Multi-agent systems aren't just academic concepts—they're practical tools for building resilient, scalable applications. The key is starting simple and evolving based on real requirements rather than theoretical perfect architectures.