9.7.1 Multi-Agent Roadmap: Roles, Messages, Owner

Multi-Agent is a division-of-labor mechanism, not several chatbots talking. Use it only when role separation, parallel work, cross-checking, or specialist collaboration is worth the coordination cost.

See the Collaboration Cost First

The key question is: does the benefit of splitting work exceed the cost of messages, repeated context, conflicts, and final merging?

Run a Role Boundary Check

Every role needs one responsibility and one output. Keep one owner for the final decision.

agents = {
    "researcher": "collect evidence",
    "editor": "rewrite content",
    "reviewer": "check beginner clarity",
}

final_owner = "reviewer"

print("agent_count:", len(agents))
for name, job in agents.items():
    print(f"{name}: {job}")
print("final_owner:", final_owner)

Expected output:

agent_count: 3
researcher: collect evidence
editor: rewrite content
reviewer: check beginner clarity
final_owner: reviewer

If two roles produce the same output, merge them. If nobody owns the final decision, the system will drift.

Learn in This Order

Step	Read	Practice Output
1	When to use Multi-Agent	Write when a single Agent is better
2	Common patterns	Compare supervisor-executor, pipeline, debate, expert committee
3	Communication	Define message format, shared state, and handoff rule
4	Coordination	Track owner, queue, conflict rule, and aggregation
5	Practice and risks	Measure cost, loops, duplicated work, and role overreach

Pass Check

You pass this chapter when a 2 to 3 Agent demo has traceable inputs, outputs, handoffs, final ownership, and a clear reason why it beats a single Agent.