How to Build Multi-Agent AI Systems for Complex Workflows

April 23, 2026•10 min read•Michael Ridland

When is one AI agent not enough? And when you need multiple agents working together, how do you build a system that's reliable enough for production?

Multi-agent AI is one of the most exciting areas in enterprise AI right now, but it's also one of the easiest to over-engineer. I'm Michael Ridland, founder of Team 400, and we build multi-agent systems for Australian businesses. Here's what we've learned about when to use them and how to build them well.

What Is a Multi-Agent System?

A multi-agent system uses multiple AI agents, each with a specific role, that collaborate to complete tasks too complex for a single agent.

Think of it like a team of specialists rather than one generalist. Instead of one agent that tries to handle customer support, billing, technical troubleshooting, and escalation, you have dedicated agents for each function that coordinate with each other.

User Request
    ↓
[Router Agent] → Determines which specialist agent(s) to involve
    ↓
[Research Agent] → Gathers relevant information
[Analysis Agent] → Processes and reasons about the information
[Action Agent] → Executes tasks in external systems
    ↓
[Synthesis Agent] → Combines outputs into a coherent response
    ↓
Response to User

Each agent has its own system prompt, tools, and scope. This specialisation leads to better performance on complex tasks.

When Do You Actually Need Multi-Agent?

This is the most important question, and the answer is "less often than you think."

Use a single agent when:

The task can be described in one clear system prompt
The tools needed are fewer than 10-15
The decision-making is relatively linear
One conversation context is sufficient

Use multi-agent when:

Different parts of the task require fundamentally different expertise
The system prompt would be unreasonably long for a single agent
You need agents to check each other's work
Different parts of the workflow need different models (e.g., a fast model for routing, a capable model for analysis)
The task involves parallel processing of independent sub-tasks
You need agents with different permission levels

In our experience, about 60% of projects that initially seem like multi-agent problems are better solved with a well-designed single agent. We always prototype as a single agent first and split into multiple agents only when we hit clear limitations.

Multi-Agent Architecture Patterns

Pattern 1 - Router and Specialists

The simplest multi-agent pattern. A router agent receives the user's request and delegates to specialist agents.

User → Router Agent → Specialist A → Response
                    → Specialist B → Response
                    → Specialist C → Response

Best for: Customer service systems where queries fall into distinct categories (billing, technical support, account management).

How it works:

Router agent analyses the request and determines the category
Request is forwarded to the appropriate specialist agent
Specialist handles the request and returns a response
Router may do final formatting or quality checks

Implementation:

router_prompt = """
You are a routing agent. Analyse the user's request and determine
which specialist should handle it.

Available specialists:
- billing: Account charges, invoices, payment issues
- technical: Product issues, troubleshooting, configuration
- account: Account changes, upgrades, cancellations

Respond with the specialist name only.
"""

specialists = {
    "billing": BillingAgent(model="gpt-4o", tools=[lookup_invoice, process_refund]),
    "technical": TechnicalAgent(model="gpt-4o", tools=[check_status, run_diagnostic]),
    "account": AccountAgent(model="gpt-4o", tools=[update_account, check_eligibility]),
}

Advantages: Simple, easy to reason about, each specialist can be tested independently.

Disadvantages: Doesn't handle requests that span multiple domains well. Router misclassification sends users to the wrong specialist.

Pattern 2 - Pipeline (Sequential Agents)

Agents process a request in sequence, each adding value.

Input → Agent A → Agent B → Agent C → Output
         (Extract)  (Analyse)  (Report)

Best for: Document processing, data analysis, report generation - workflows with clear sequential stages.

Example - Compliance Review Pipeline:

Extraction Agent - Reads the document and extracts key data points
Rules Agent - Checks extracted data against compliance rules
Risk Assessment Agent - Evaluates overall risk based on findings
Report Agent - Generates a structured compliance report

Each agent receives the output of the previous agent plus the original document context.

Advantages: Clear flow, easy to debug (you can inspect the output at each stage), stages can use different models optimised for their task.

Disadvantages: Slow (sequential execution), errors compound through the pipeline, no feedback loops.

Pattern 3 - Supervisor and Workers

A supervisor agent manages a team of worker agents, assigning tasks and reviewing results.

        [Supervisor Agent]
       /    |    |    \
  Worker  Worker Worker Worker
    A       B      C      D

Best for: Complex research tasks, multi-step investigations, tasks that require coordination of parallel work.

How it works:

Supervisor receives the task and creates a plan
Supervisor assigns sub-tasks to worker agents
Workers execute independently and return results
Supervisor reviews results, may request revisions
Supervisor synthesises final output

Implementation approach:

supervisor_prompt = """
You are a supervisor agent managing a team of research workers.

Given a research question, you should:
1. Break it into sub-questions
2. Assign each to a worker agent
3. Review the results for quality
4. Synthesise into a final answer

Available workers:
- data_researcher: Searches internal databases and documents
- web_researcher: Searches external sources
- analyst: Performs calculations and data analysis
- writer: Drafts well-structured content

You can assign tasks, review outputs, and request revisions.
"""

Advantages: Handles complex, open-ended tasks well. Supervisor catches errors. Parallel execution possible.

Disadvantages: More complex to implement and debug. Supervisor agent needs to be highly capable. Higher token usage.

Pattern 4 - Debate and Consensus

Two or more agents independently process the same request, then a judge agent evaluates and selects the best response.

Input → Agent A (Independent) → Judge Agent → Output
      → Agent B (Independent) →

Best for: High-stakes decisions where accuracy is critical. Risk assessment, medical classification, legal analysis.

How it works:

Same input goes to multiple agents with different prompts or perspectives
Each produces an independent analysis
Judge agent compares outputs, identifies disagreements
Judge resolves disagreements or flags for human review

Advantages: Catches errors that a single agent would miss. Increases confidence in outputs. Natural quality control.

Disadvantages: Multiplies cost and latency. Judge agent needs to be highly capable. Sometimes disagreements are genuine ambiguity, not errors.

Building Blocks for Multi-Agent Systems

Agent Communication

Agents need a structured way to pass information between each other.

Shared context/memory: All agents read from and write to a shared state object. Simple but can get messy.

shared_state = {
    "user_request": "...",
    "extracted_data": {},  # Written by extraction agent
    "analysis_results": {},  # Written by analysis agent
    "recommendations": [],  # Written by recommendation agent
}

Message passing: Agents send structured messages to each other. More formal, better for debugging.

message = {
    "from": "extraction_agent",
    "to": "analysis_agent",
    "type": "extraction_complete",
    "data": {"fields": {...}, "confidence": 0.92},
    "timestamp": "2026-04-23T10:15:30Z"
}

Event-driven: Agents subscribe to events and react. Most decoupled, best for systems that need to scale.

We typically use shared context for simpler systems and message passing for anything running in production at scale.

Tool Design

Each agent should only have access to the tools it needs. This is both a security principle and a performance one - fewer tools means the LLM makes better tool-use decisions.

Tool isolation example:

billing_agent_tools = [
    lookup_invoice,
    process_refund,
    check_payment_status,
    calculate_prorated_amount
]

technical_agent_tools = [
    check_system_status,
    run_diagnostic,
    lookup_knowledge_base,
    create_support_ticket
]

# Each agent only sees its own tools
# The billing agent can't run diagnostics
# The technical agent can't process refunds

Error Handling and Recovery

Multi-agent systems have more failure modes than single-agent systems. Plan for them.

Agent failure: If one agent fails (timeout, error, hallucination), the system needs a fallback. Options:

Retry with the same agent
Fall back to a simpler agent or a rule-based system
Escalate to human review
Return a partial result with an explanation

Disagreement between agents: When agents produce conflicting outputs, you need a resolution strategy:

Defer to the more specialised agent
Use a judge agent to decide
Flag for human review
Return both outputs with confidence scores

Infinite loops: Agents that can call each other can create loops. Always set maximum iteration limits and total token budgets.

MAX_AGENT_ITERATIONS = 10
MAX_TOTAL_TOKENS = 50000

for iteration in range(MAX_AGENT_ITERATIONS):
    if total_tokens > MAX_TOTAL_TOKENS:
        return fallback_response()
    result = agent.execute(context)
    if result.is_complete:
        break

Observability

You absolutely need detailed logging for multi-agent systems. When something goes wrong (and it will), you need to trace the full agent interaction chain.

Log every:

Agent invocation (which agent, what input, what output)
Tool call (which tool, parameters, result)
Decision point (why did the router choose agent B over agent A?)
Token usage per agent
Latency per agent

We covered this in detail in our AI monitoring and observability guide. For multi-agent systems, it's even more important.

Framework Choices

Several frameworks support multi-agent development. Here's what we recommend.

AutoGen (Microsoft): Good for conversational multi-agent patterns. Agents can have group chats. Best suited for collaborative scenarios. Well-integrated with Azure.

LangGraph (LangChain): Graph-based agent orchestration. Define agents as nodes, transitions as edges. Good for complex conditional workflows. We use this often for pipeline and supervisor patterns.

Semantic Kernel (Microsoft): .NET-native with Python support. Strong Azure integration. Good for enterprise environments already using the Microsoft stack.

CrewAI: Higher-level framework focused on role-based agent teams. Quick to prototype but less flexible for production customisation.

For Australian enterprise clients on Azure, we typically use LangGraph or Semantic Kernel depending on whether the team is Python or .NET focused.

A Real Example - Insurance Claims Processing

Here's a multi-agent system we designed for an Australian insurance company.

Agents:

Triage Agent - Classifies the claim type and determines processing path. Uses GPT-4o-mini for fast classification.
Document Agent - Extracts data from claim documents (policy, damage photos, receipts, reports). Uses Azure AI Document Intelligence and GPT-4o.
Validation Agent - Checks extracted data against policy terms, coverage limits, and business rules. Uses GPT-4o with access to policy database.
Assessment Agent - Evaluates the claim based on all gathered information and recommends approval, rejection, or further investigation. Uses GPT-4o.
Communication Agent - Generates customer-facing communications about claim status. Uses GPT-4o-mini.

Flow:

Claim Submission → Triage → Document Extraction → Validation → Assessment → Communication
                                    ↓ (if documents missing)
                              Request additional documents from customer

Results: Processing time dropped from 5 days average to 4 hours for straightforward claims. Complex claims still go to human assessors, but with all data pre-extracted and validated.

Getting Started

Start with a single agent. Build the simplest version that works. Identify where it fails or struggles.
Split at failure points. Where the single agent consistently makes mistakes, consider splitting that responsibility to a specialist agent.
Keep the architecture as simple as possible. Two agents with clear roles beats five agents with overlapping responsibilities.
Test each agent independently. Before testing the full system, verify each agent performs its specific task well in isolation.
Monitor everything. Multi-agent systems need more observability than single-agent ones, not less.

If you're building multi-agent AI systems, talk to our team. We design and implement multi-agent architectures for Australian businesses, from initial design through to production deployment. Explore our AI agent development services and AI consulting to learn more about our approach.