Multi-Agent Architectures

When a single agent is not enough, multiple agents can collaborate on complex tasks. Multi-agent systems divide work across specialised agents, each with distinct roles, tools, and expertise. This lesson covers the major architectural patterns, communication protocols, and when to choose multi-agent over single-agent designs.

---

Why Multi-Agent?

Challenge with Single Agent	How Multi-Agent Helps
Too many tools (context bloat)	Each agent has a focused tool set
No specialisation	Agents can be experts in specific domains
Single point of failure	Other agents can compensate
Complex tasks overwhelm one model	Divide and conquer
Hard to scale	Add more agents as complexity grows

---

Architecture Patterns

Pattern 1: Supervisor

A single supervisor agent delegates tasks to worker agents and aggregates results.

                 ┌──────────────┐
                 │  SUPERVISOR  │
                 │  (orchestrator)│
                 └──────┬───────┘
                        │
            ┌───────────┼───────────┐
            │           │           │
            ▼           ▼           ▼
     ┌───────────┐ ┌──────────┐ ┌───────────┐
     │ Research  │ │ Analysis │ │  Writing   │
     │ Agent     │ │ Agent    │ │  Agent     │
     └───────────┘ └──────────┘ └───────────┘

from openai import OpenAI
import json

client = OpenAI()