How to Build a Multi-Agent System (Part 1/3): From Problem to Design
- Cristian Dordea
- Oct 6
- 8 min read
This is Part 1 of a 3-part series on building production multi-agent systems from scratch
(Part 2/3): From Architecture to Implementation
(Part 3/3): From Evaluation to Production
Intro
Building a multi-agent system might sound complex, but it follows a logical progression from understanding your problem to deploying a solution. Let's walk through this journey using a real-world example: transforming a struggling customer service operation into an intelligent, efficient system.
The Problem Worth Solving
We will use a fictional SaaS company as an example to explain the concepts of Multi-Agent System design more practically.
Our fictional SaaS company faces a challenge familiar to many enterprises: its customer service is drowning. With over 500 daily support tickets, customers wait 24-48 hours for responses, and 30% require multiple interactions to resolve their issues. Support agents spend 60% of their time just gathering information before they can even start solving problems.
This scenario is perfect for a multi-agent system because it involves diverse expertise (technical, billing, logistics), multiple data sources, and both predictable and unpredictable elements. Let's see how to transform this chaos into coordination.
Understanding Multi-Agent Systems
Forget the buzzwords for a moment. An AI agent is a specialist who can "think, decide, and act." Not a chatbot following a script, but something that can actually use logic sequencing to reason through problems.
That is an AI agent, so what's a multi-agent system? That's a team of these specialists working together.
Think about your favorite restaurant. You don't have one person trying to greet you, take your order, cook your food, and handle the bill. You have hosts, servers, chefs, and managers, each excellent at their specific job, and all coordinating to create your experience. That's what we're building, but with the help of AI.
Why Multiple Agents Matter
Single agents fall short when faced with complex, broader tasks. Multi-agent systems solve this through:
Each agent masters one thing: Your Technical Support Agent becomes genuinely skilled at troubleshooting, because that's all they do. No confusion about billing, no mixing up shipping details, just pure technical expertise.
They work simultaneously: while one agent determines the cause, another reviews documentation, and a third examines previous cases. What took hours happens in seconds.
They actually collaborate: When a customer has both a billing and technical issue, both specialists work together, sharing just enough context to solve the problem holistically.
When Multi-Agent Makes Sense
Not every problem needs lots of agent specialists. Use multiple agents when you have:
Diverse expertise required: Technical AND billing AND logistics.
Parallel workstreams possible: Multiple independent tasks.
Clear handoff points: Distinct stages where different expertise takes over.
Scale justifies complexity: Volume that makes orchestration overhead worthwhile.
Phase 1: Foundation and Problem Analysis
Document Your Current Reality
The first step is documenting what actually happens with each ticket. Not the idealized process in the training manual, but the messy reality:
Customer submits a ticket
Sits in queue (12-24 hours)
Agent picks up the ticket
Agent searches 5+ systems for context (45 minutes)
Agent drafts response
Supervisor reviews (if needed)
Response sent (24-48 hours total)
Identify the Pain Points
Where does this process break?
The tech support company identified several critical issues:
Information silos: Agents manually check CRM, billing system, order management, knowledge base, and ticket history
No prioritization: Urgent "system down" tickets wait behind password resets
Inconsistent quality: Different agents give different solutions to identical problems
No learning loop: Solved cases aren't systematically captured for future use
Define Success Metrics
Define what success looks like, not vague improvements, but specific targets.
Be specific:
Reduce response time: 48 hours → 5 minutes
Increase first-contact resolution: 40% → 85%
Reduce cost per ticket: $12 → $0.20
Improve satisfaction score: 3.2 → 4.5
These aren't arbitrary; they're based on industry benchmarks and competitive requirements.
Reality Check Before Moving to Part 2
[ ] Can you name the top 3 pain points in your current workflow?
[ ] Have you identified which tasks are "expertise" v. "busywork"?
[ ] Do you know which 2-3 agents would provide the most immediate value?
[ ] Can you explain why this needs multiple agents instead of one smart one?
If you answered "no" to any of these, spend more time in the problem analysis phase. The implementation will be much smoother.
Phase 2: Design Your Agent Team
Shift Your Mental Model
Here's where most people get it wrong, and I made this mistake too. The first instinct is to enhance the existing process by making each step faster with AI. That's like replacing horses with faster horses instead of inventing the car.
The breakthrough came when we stopped thinking about steps and started thinking about expertise. Not "what needs to happen?" but "who would we hire if we could hire anyone?"
Traditional approach:
Customer → Queue → Agent → Research → Respond
Multi-agent approach:
Customer → Parallel classification + Research → Specialized resolution → Validated response
Define Your Specialist Agents
I designed 11 specialist agents for our customer service system. Each has one job, and they're really good at it.
📌 A Note on Complexity: Start Small, Think Big Seeing 11 specialized agents might feel overwhelming. "Am I supposed to build all of this at once?" Absolutely not. We're defining the complete system now so you understand the full picture and how each piece fits together. Think of this as your architectural blueprint, showing you the destination before we start the journey.
In Part 2, we'll show you exactly how to implement just 3 core agents to start. In Part 3, you'll learn a phased deployment strategy that proves value incrementally. Most successful multi-agent systems begin with 2-3 agents and expand only after demonstrating clear ROI.
1. Request Classifier Agent
Role: Triage specialist who categorizes all incoming requests
One Job: Determine if this is a technical, billing, order, or feature request
Output: Category with confidence score
2. Customer Service Orchestrator Agent
Role: Workflow coordinator
One Job: Manage the entire resolution process
Output: Coordinated team response
3. Urgency Detector Agent
Role: Crisis identifier
One Job: Spot time-sensitive issues ("system down," "losing money")
Output: Priority level (Critical/High/Medium/Low)
4. Technical Support Agent
Role: Senior engineer
One Job: Solve product and integration issues
Output: Step-by-step technical solutions
5. Billing Agent
Role: Financial information specialist
One Job: Answer questions about payments, subscriptions, and invoices
Output: Clear explanations of billing situations and available options (no direct payment modifications)
6. Order Agent
Role: Logistics coordinator
One Job: Manage shipping, returns, exchanges
Output: Order status and next steps
7. Knowledge Retrieval Agent
Role: Documentation expert
One Job: Find relevant documentation
Output: Precise document excerpts
8. Case History Agent
Role: Pattern analyst
One Job: Find similar resolved cases
Output: Top 3 similar cases with solutions
9. Solution Validator Agent
Role: Quality controller
One Job: Verify solution completeness and accuracy
Output: Approval or specific revision requests
10. Response Generator Agent
Role: Communication specialist
One Job: Create customer-appropriate responses
Output: Professional, empathetic customer message
11. Escalation Agent
Role: Senior manager
One Job: Determine when humans should intervene
Output: Escalation decision with routing
Example: Technical Support Agent System Prompt:
You are a senior technical support specialist for #enter company name and platform here#.
ROLE: Diagnose and resolve technical issues with our API, integrations, and platform features.
CAPABILITIES:
- Access to: API documentation, error code database, common solutions playbook
- Can query: System status, user configuration, recent error logs
- Cannot: Modify user data, change billing, access other customers' information
REASONING APPROACH:
1. First, identify the specific technical component involved
2. Check for known issues or system-wide problems
3. Gather relevant error messages and timestamps
4. Propose solution with step-by-step instructions
5. If confidence < 70%, escalate to human engineer
OUTPUT FORMAT:
- Problem identified: [specific issue]
- Root cause: [technical explanation]
- Solution steps: [numbered list]
- Confidence level: [percentage]Implementation Note: The confidence percentage can come from the LLM's self-assessment (by instructing it to evaluate its own certainty), or from your orchestrator's evaluation of response completeness. In practice, we use a combination—the agent self-reports confidence, and our orchestrator validates this against response quality checks.
Map Information Flow
This system approach actually works because each agent only gets the information it needs. The Technical Support Agent doesn't get billing history. The Billing Agent doesn't see technical logs. This focus makes each agent more accurate, not less.
It's counterintuitive, we usually think more context is better. But imagine trying to cook dinner while someone reads you their tax returns. Irrelevant information is noise, and noise causes mistakes.
Classifier Agent receives: Raw customer query
Urgency Detector Agent receives: Query + customer tier
Specialist Agents receive: Categorized issue + relevant history
Solution Validator Agent receives: Proposed solution + requirements
Response Generator Agent receives: Validated solution + tone preferences
This focused approach prevents information overload and improves accuracy. Here is an example visual that represents this point. The example below represents just a simplified information flow which does not include all 11 agents.
Note: The priority level from the Urgency Agent affects processing speed and resource allocation, but each agent still only sees the information relevant to its task. A CRITICAL priority doesn't mean the Technical Agent suddenly gets access to billing data—it just knows to process this request immediately.
Choose Coordination Patterns
Different parts of your workflow need different patterns. Think of these as the "playbook" for how your agents work together—each pattern solves a specific collaboration challenge.
The key insight: you don't use one pattern for everything. Routing handles diversity, parallel processing handles speed, sequential ensures quality gates, and evaluator-optimizer ensures excellence. Mix and match based on what each part of your workflow needs to achieve.
I used all four to show a more comprehensive solution: routing to handle different issue types, parallel for faster research, sequential for quality control, and evaluator-optimizer when the first response wasn't quite right. Each pattern earned its place by solving a real problem we discovered during testing.
What We've Built So Far
You now have the blueprint for your multi-agent system. We've identified the problem worth solving, designed specialized agents with clear responsibilities, and mapped out how they'll coordinate. We also provided you with a prompt example for one of the agents. This is your North Star—every implementation decision should trace back to these design choices.
What's Next:
From Architecture to Implementation In Part 2, we'll transform this design into working architecture. You'll learn:
How to choose between centralized orchestration and peer-to-peer coordination
The two types of memory your agents need (and why starting simple will hurt you later)
How to build security from day one with access controls and protection layers
The three validation gates that prevent cascade failures
How to implement your first 3 agents with proper orchestration
We'll start with the Classifier, Technical Support, and Response Generator agents, the core trio that can deliver value in week one. You'll see exactly how validation gates catch errors before they cascade, including real examples of multi-domain requests and incomplete responses. By the end of Part 2, you'll have a working system with security baked in, not bolted on. Stay tuned.
About me
Cristian, Cristian spent over 12 years leading data and software teams in delivering large-scale, complex projects and initiatives exceeding $ 20 M for Fortune 500 companies, including FOX Corporation, Ford, Stellantis, Slalom, and Manheim. At FOX, he scaled Agile delivery across 60+ data professionals, architecting AI/ML solutions, including recommendation engines and identity graph systems.
Now specializing as an AI Product & Delivery Leader focused on AI Agent solutions and enterprise-scale transformations, Cristian is currently building production-ready Multi-Agent AI systems using AWS GenAI stack, CrewAI, and RAG architectures.
Bridging technical depth with business strategy, he completed intensive training in Agentic AI Engineering and AI Product Management, mastering multi-agent architecture design, orchestration, agentic workflows, advanced prompt engineering, and AI agent evaluations.
This unique combination of scaled enterprise delivery experience and hands-on AI agent development enables him to translate complex AI capabilities into measurable business outcomes.
Certifications: AWS Certified AI Practitioner | Agentic AI (Udacity) | AI Product Management | Databricks GenAI | Azure AI Fundamentals | SAFe 5 SPC | Data-Driven Scrum for AI/ML projects
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