Building a Multi-Agent System: From Single Bot to Coordinated Team

When our customer service bot started buckling under complex requests that required multiple steps—like processing a refund that needed inventory checks, payment verification, and email confirmations—we knew we needed a different approach.

Our solution was a multi-agent system where specialized agents handle distinct tasks and coordinate through a simple message-passing architecture. Here's how we built it.

The Problem: One Bot, Too Many Jobs

Our original bot tried to do everything:

• Answer product questions
• Process refunds
• Check inventory
• Send notifications
• Update databases

This led to:

• 45-second average response times
• Frequent timeouts on complex requests
• Difficult debugging when things went wrong
• Hard-to-maintain monolithic code

The Multi-Agent Architecture

We broke our system into five specialized agents:

1. Orchestrator Agent

• Routes incoming requests
• Manages workflow state
• Coordinates between other agents

2. Knowledge Agent

• Handles FAQ and product information
• Maintains searchable knowledge base
• Returns structured responses

3. Transaction Agent

• Processes refunds and payments
• Validates financial data
• Interfaces with payment APIs

4. Inventory Agent

• Checks stock levels
• Reserves items
• Updates availability

5. Notification Agent

• Sends emails and SMS
• Manages communication templates
• Tracks delivery status

Concrete Example: Processing a Refund Request

Here's how a typical refund request flows through our system:

1. Customer: "I want to return my order #12345"

2. Orchestrator → Knowledge Agent: "What's our return policy?"
   Knowledge Agent → Orchestrator: "30-day returns, original packaging required"

3. Orchestrator → Transaction Agent: "Lookup order #12345"
   Transaction Agent → Orchestrator: "Order found, $89.99, within return window"

4. Orchestrator → Inventory Agent: "Can we restock item XYZ-001?"
   Inventory Agent → Orchestrator: "Yes, shelf space available"

5. Orchestrator → Transaction Agent: "Process refund for $89.99"
   Transaction Agent → Orchestrator: "Refund processed, transaction ID: TXN789"

6. Orchestrator → Notification Agent: "Send refund confirmation"
   Notification Agent → Orchestrator: "Email sent to customer"

7. Orchestrator → Customer: "Your refund has been processed. You'll see $89.99 in your account within 3-5 business days."

Implementation Details

Message Queue Architecture

We use Redis as our message broker with a simple request-response pattern:

# Agent communication structure
{
  "id": "req_12345",
  "from": "orchestrator",
  "to": "transaction_agent",
  "action": "lookup_order",
  "data": {"order_id": "12345"},
  "timestamp": "2024-01-15T10:30:00Z"
}

Agent Base Class

Each agent inherits from a common base that handles:

• Message listening and routing
• Error handling and retries
• Logging and monitoring
• Health checks

State Management

The Orchestrator maintains conversation state in Redis with a 30-minute TTL:

conversation_state = {
  "user_id": "user_789",
  "current_step": "awaiting_confirmation",
  "context": {
    "order_id": "12345",
    "refund_amount": 89.99,
    "transaction_id": "TXN789"
  }
}

Key Design Decisions

1. Synchronous Communication

We chose request-response over pub-sub for predictable workflows and easier debugging.

2. Centralized Orchestration

Rather than peer-to-peer communication, the Orchestrator manages all inter-agent coordination to prevent circular dependencies.

3. Stateless Agents

All agents except the Orchestrator are stateless, making them easier to scale and test.

4. Timeout Handling

Each request has a 10-second timeout with exponential backoff retry logic.

Results After Implementation

• Response time: 45 seconds → 18 seconds average
• Success rate: 87% → 96%
• Development speed: New features ship 3x faster
• Debugging: Issues isolated to specific agents
• Scaling: Individual agents can be scaled based on load

Lessons Learned

What Worked Well

• Clear separation of concerns made debugging straightforward
• Individual agents could be developed and deployed independently
• System remained responsive even when one agent was slow

What We'd Do Differently

• Add circuit breakers earlier to handle agent failures gracefully
• Implement better observability from day one
• Start with fewer agents and split as needed

Next Steps

We're now exploring:

• Adding a learning agent that improves responses based on customer feedback
• Implementing dynamic agent spawning for high-load scenarios
• Building a visual workflow editor for non-technical team members

Getting Started

If you're considering a multi-agent approach:

1. Start small: Begin with 2-3 agents maximum
2. Define clear boundaries: Each agent should have a single, well-defined responsibility
3. Plan for failure: Agents will go down; design for graceful degradation
4. Monitor everything: Distributed systems are harder to debug
5. Test agent interactions: Integration testing becomes critical

Multi-agent systems aren't magic, but they can transform complex workflows into manageable, scalable components when designed thoughtfully.