Building a Multi-Agent System: From Concept to Production

Last year, we faced a problem: our customer support team was drowning in repetitive tickets while complex issues went unresolved. We decided to build a multi-agent system to help triage, categorize, and resolve common problems automatically.

Here's how we approached it, what worked, and what we learned.

The Problem We Were Solving

Our support team received 500+ tickets daily:

• 40% were password resets or account lockouts
• 30% were billing questions with standard answers
• 20% were feature requests or bug reports requiring categorization
• 10% were complex technical issues needing human expertise

The manual process took 2-3 hours per ticket on average, with significant variation in quality and response time.

System Architecture

We designed a system with four specialized agents:

1. Intake Agent

Responsible for initial ticket processing and routing.

Capabilities:

• Extract key information (customer ID, issue type, urgency)
• Perform initial classification
• Route to appropriate specialist agent
• Handle malformed or incomplete tickets

2. Resolution Agent

Handles straightforward issues that can be resolved automatically.

Capabilities:

• Password resets and account unlocks
• Standard billing inquiries
• Knowledge base lookups
• Generate and send resolution emails

3. Analysis Agent

Processes tickets requiring categorization or data extraction.

Capabilities:

• Extract feature requests and bug reports
• Categorize issues by product area
• Identify trending problems
• Create structured summaries for human review

4. Escalation Agent

Manages handoffs to human agents and tracks complex cases.

Capabilities:

• Determine when human intervention is needed
• Prepare context and summaries for human agents
• Monitor case progress and follow up
• Learn from human resolutions to improve future routing

Coordination Pattern

We implemented a message-passing system with a central coordinator:

[Ticket] → [Coordinator] → [Intake Agent]
                ↓
[Coordinator] ← [Classification Result]
                ↓
[Appropriate Specialist Agent] ← [Routed Ticket]
                ↓
[Coordinator] ← [Agent Result]
                ↓
[Database] + [Customer Notification]

Message Format

Each message includes:

• Ticket ID and customer context
• Current processing state
• Agent-specific data payload
• Error handling information
• Audit trail

State Management

We used a simple state machine:

1. Received - New ticket in system
2. Processing - Agent actively working
3. Resolved - Automatic resolution complete
4. Escalated - Handed to human agent
5. Closed - Customer confirmed resolution

Implementation Details

Agent Communication

We built agents as separate microservices communicating via message queues:

• Message Queue: Redis with retry logic
• State Storage: PostgreSQL for ticket state and audit logs
• Agent Memory: Each agent maintains context in Redis with TTL
• Monitoring: Custom metrics on processing time, success rate, and escalation frequency

Error Handling

Each agent implements:

• Graceful degradation: Fall back to simpler processing if complex analysis fails
• Dead letter queues: Capture failed messages for manual review
• Circuit breakers: Prevent cascade failures when external services are down
• Timeout handling: Escalate if processing takes too long

Concrete Example: Password Reset Flow

Here's how a typical password reset request flows through the system:

1. Customer submits: "I can't log into my account. My username is john@example.com"

2. Intake Agent processes:

   • Extracts email: john@example.com
   • Classifies as: authentication_issue
   • Confidence: 0.94
   • Routes to: Resolution Agent

3. Resolution Agent handles:

   • Validates email exists in customer database
   • Checks account status (not suspended/closed)
   • Generates secure reset token
   • Sends reset email to customer
   • Updates ticket status to "resolved"
   • Total processing time: 3.2 seconds

4. Follow-up: System monitors if customer successfully resets password within 24 hours

Results and Metrics

After six months in production:

Performance Improvements

• Average resolution time: 3.2 seconds (was 2+ hours)
• Automatic resolution rate: 68% of all tickets
• Human agent time saved: 15 hours/day
• Customer satisfaction: Increased from 3.2 to 4.1 (5-point scale)

Cost Breakdown

• Infrastructure: $200/month (Redis, additional compute)
• Development time: 3 engineers × 4 months
• Ongoing maintenance: ~4 hours/week

Lessons Learned

What Worked Well

1. Clear agent boundaries: Each agent had a specific, well-defined role
2. Graceful degradation: System continued working even when individual agents failed
3. Human oversight: Kept humans in the loop for quality control and learning
4. Incremental rollout: Started with 10% of tickets, gradually increased

What We'd Do Differently

1. Better observability from day one: Spent too much time debugging without proper tracing
2. More sophisticated routing logic: Initial keyword-based classification was too brittle
3. Customer feedback loop: Should have collected customer satisfaction data sooner
4. Agent specialization: Some agents tried to do too much; simpler is better

Common Pitfalls to Avoid

• Over-engineering coordination: Started with complex orchestration, simplified to message passing
• Ignoring edge cases: 5% of tickets had unusual formats that broke early versions
• Insufficient testing: Production revealed interaction patterns we hadn't anticipated
• Poor error messages: Customers were confused when escalation happened without explanation

Next Steps

We're currently working on:

• Sentiment analysis: Route frustrated customers to senior agents faster
• Learning from resolutions: Agents learn from human corrections to improve over time
• Proactive support: Identify potential issues before customers report them
• Multi-language support: Expand beyond English-only tickets

Key Takeaways

Building a successful multi-agent system requires:

1. Clear problem definition with measurable success criteria
2. Simple agent responsibilities rather than complex, multi-purpose agents
3. Robust error handling and graceful degradation
4. Human oversight for quality control and continuous improvement
5. Incremental deployment with careful monitoring

The investment paid off quickly in our case, but success depends heavily on having well-defined, repetitive processes that can be automated. Start small, measure everything, and keep humans involved in the process.