Multi-Agent Architectures

When One Agent Isn't Enough

Complex tasks benefit from multiple specialized agents. But "just add more agents" is a recipe for chaos. You must choose the right coordination pattern based on the task structure.

1. Supervisor Pattern

A single "manager" agent receives the user request, plans the subtasks, delegates to specialized worker agents, and aggregates results.

Best for: Complex orchestration with clear subtask decomposition. Reports, analysis, code generation.

2. Pipeline Pattern

Agents process sequentially. Each agent refines the output of the previous one. Think assembly line.

Best for: Content creation, CI/CD automation, data processing pipelines.

3. Debate Pattern

Multiple agents argue opposing perspectives and a judge agent selects the best argument. Reduces hallucination through adversarial verification.

Best for: Fact-checking, risk analysis, decision-making under uncertainty.

4. Swarm Pattern (OpenAI)

Agents self-organize without a central coordinator. Each agent has a handoff() function to transfer control to another agent when the task is outside its scope.

Best for: Customer support with departments, triage bots, open-ended workflows.

Which Pattern Should You Use?

Pattern	Control	Latency	When to Use
Supervisor	Centralized	Higher	Complex orchestration with subtask planning
Pipeline	Sequential	Predictable	Linear workflows (generate → review → publish)
Debate	Adversarial	High	High-stakes decisions, fact verification
Swarm	Decentralized	Low	Dynamic routing, customer support triage

1from openai import OpenAI
2
3client = OpenAI()
4
5AGENTS = {
6    "sales": {
7        "system": "You are a sales agent. If user asks about refunds, handoff to refund_agent.",
8        "tools": ["check_inventory", "process_order"]
9    },
10    "refund": {
11        "system": "You are a refund agent. Process return requests.",
12        "tools": ["lookup_order", "process_refund"]
13    }
14}
15
16def run_agent(agent_name: str, messages: list) -> dict:
17    agent = AGENTS[agent_name]
18    response = client.chat.completions.create(
19        model="gpt-4o",
20        messages=[
21            {"role": "system", "content": agent["system"]},
22            *messages
23        ]
24    )
25    reply = response.choices[0].message.content
26    
27    # Simple handoff detection
28    if "handoff to refund_agent" in reply.lower():
29        print(f"Handoff: {agent_name} -> refund")
30        return run_agent("refund", messages)
31    
32    return {"agent": agent_name, "response": reply}
33
34result = run_agent("sales", [
35    {"role": "user", "content": "I want to return my order #12345"}
36])