Building a Multi-Agent System: Lessons from Real Production

Six months ago, our customer support team was drowning in tickets. We had three main problems: technical questions needed engineering expertise, billing issues required account access, and general inquiries could be handled by anyone. The inefficiency was killing us.

We decided to build a multi-agent system to automatically route and handle these requests. Here's what we learned.

The Problem We Solved

Our support inbox received 500+ tickets daily across three categories:

• Technical issues (40%): API errors, integration problems, configuration questions
• Billing inquiries (25%): Subscription changes, invoice disputes, payment failures
• General support (35%): Feature requests, account questions, how-to guides

Each type needed different expertise and tools. Engineers shouldn't handle billing, and billing specialists couldn't debug API calls.

System Architecture

Core Components

We built four specialized agents:

1. Router Agent: Classifies incoming tickets and assigns them
2. Technical Agent: Handles API documentation, code examples, debugging
3. Billing Agent: Processes subscription changes, invoice lookups
4. General Agent: Manages FAQs, feature requests, account questions

Coordination Pattern

We used a hub-and-spoke model rather than peer-to-peer communication:

Incoming Ticket → Router Agent → Specialized Agent → Response
                     ↓
                Supervisor Agent (monitors quality)

This simplified debugging and prevented circular conversations between agents.

Technology Stack

• Message Queue: Redis for ticket routing
• Agent Framework: Custom Python service using OpenAI's API
• Knowledge Base: Vector database (Pinecone) with company docs
• Monitoring: Custom dashboard tracking resolution rates and response times

Implementation Details

Router Agent Logic

The router uses a combination of keyword matching and semantic similarity:

def classify_ticket(content):
    # First pass: keyword matching
    if any(word in content.lower() for word in ['api', 'error', 'integration']):
        return 'technical'
    
    if any(word in content.lower() for word in ['billing', 'invoice', 'payment']):
        return 'billing'
    
    # Second pass: semantic classification
    embedding = get_embedding(content)
    similarity_scores = compare_to_examples(embedding)
    
    return max(similarity_scores, key=similarity_scores.get)

Agent Specialization

Each agent has access to different tools and knowledge:

• Technical Agent: API documentation, error code database, sample code repository
• Billing Agent: Customer database, subscription management API, invoice system
• General Agent: FAQ database, feature roadmap, account management tools

Quality Control

A supervisor agent reviews responses before sending:

• Checks for hallucinations by verifying facts against source documents
• Ensures responses match the ticket category
• Flags complex cases for human review

Concrete Example: Handling an API Error

Incoming ticket:

"I'm getting a 401 error when calling the /users endpoint. My API key should be valid."

Router Agent decision:

• Keywords detected: "401 error", "API", "endpoint"
• Classification: Technical (confidence: 0.92)
• Routed to Technical Agent

Technical Agent response:

• Searches knowledge base for "401 error users endpoint"
• Finds relevant documentation about authentication headers
• Generates response with code example and troubleshooting steps
• Includes link to API documentation

Supervisor review:

• Verifies code example against actual API spec
• Confirms troubleshooting steps are current
• Approves for sending (total processing time: 8 seconds)

Results After Six Months

Performance Metrics

• Resolution time: 2.3 minutes average (down from 4 hours)
• Accuracy: 87% of responses required no human intervention
• Customer satisfaction: 4.2/5 rating (up from 3.1/5)
• Cost: 60% reduction in support staff time

What Worked Well

1. Clear agent boundaries: Each agent had specific expertise
2. Centralized routing: Prevented agents from "fighting" over tickets
3. Human oversight: Supervisor agent caught most errors before customers saw them
4. Incremental rollout: We started with 10% of tickets and gradually increased

Challenges We Faced

1. Context switching: Agents sometimes lost important details when handing off tickets
2. Knowledge updates: Keeping each agent's knowledge base current required ongoing work
3. Edge cases: Unusual tickets that didn't fit clean categories still needed human review
4. Cost management: API calls added up quickly with high ticket volume

Key Lessons

Start Simple

Our first version had just two agents: router and responder. We added specialization after understanding our actual usage patterns.

Monitor Everything

We tracked:

• Classification accuracy by the router
• Response quality ratings from customers
• Agent resource usage and costs
• Cases requiring human escalation

Build in Human Oversight

Even with high accuracy, having humans review edge cases and provide feedback improved the system continuously.

Design for Debugging

Centralized logging and clear agent responsibilities made troubleshooting much easier than a mesh of communicating agents.

What's Next

We're working on:

• Learning from interactions: Training the router on misclassified tickets
• Proactive support: Using the technical agent to identify common issues from error logs
• Multi-language support: Expanding beyond English-only tickets

Takeaways

Building a multi-agent system taught us that success comes from clear responsibilities, robust monitoring, and gradual improvement rather than trying to automate everything at once.

The key insight: treat agents like specialized team members, not general-purpose chatbots. Give them specific tools, clear boundaries, and good supervision.

Starting with your most repetitive, well-defined problems will give you the fastest wins and clearest learning opportunities.