Lesson 14: Multi-Agent Orchestration — Architecture & Design Patterns

Build AI Agents: From Zero to Production

Part 5: MCP, A2A & Multi-Agent Systems

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Introduction

Multi-agent systems are the future of AI applications. But orchestrating multiple agents is much more complicated than single agent. This article covers design patterns and challenges when building multi-agent systems.

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1. Orchestration Patterns

1.1 Sequential (Pipeline)

Agent A → Agent B → Agent C → Output

1.2 Parallel (Fan-out/Fan-in)

           ┌→ Agent B ─┐
Agent A ──►├→ Agent C ─┤──► Agent E
           └→ Agent D ─┘

1.3 Hierarchical (Supervisor)

         Supervisor
        /    |     \
    Agent A  Agent B  Agent C

1.4 Swarm (Decentralized)

Agent A ←→ Agent B
  ↕           ↕
Agent C ←→ Agent D

2. Coding Team Example

# PM analyzes requirements → Developer writes code → Reviewer reviews
pm_agent = Agent(role="Product Manager", ...)
dev_agent = Agent(role="Senior Developer", ...)  
reviewer_agent = Agent(role="Code Reviewer", ...)

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Summary

• 4 patterns: Sequential, Parallel, Hierarchical, Swarm
• Most popular Supervisor pattern for production
• Handle conflicts: priority queue, voting, escalation
• Error propagation: fail gracefully, no cascade

Exercises

1. Implement coding team: PM → Developer → Reviewer
2. Build parallel research system (3 agents search at the same time)
3. Implement supervisor pattern with LangGraph
4. Handle deadlock scenario when 2 agents wait for each other