ai-agent-book/slides/lesson-40.md

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theme	title	info	author	transition	mdc	lineNumbers	monaco	aspectRatio	canvasWidth	layout	class
seriph	Lesson 40 — Who Should Coordinate Independent Agents?	English video course for AI Agents in Depth	Bojie Li	slide-left	true	false	false	16/9	980	cover	cover

Expand · Chapter 10 · Multi-Agent Collaboration

Who Should Coordinate Independent Agents?

Peer review, managers, decentralized handoffs, files, and control planes

Lesson 40 of 42 · 17 minutes · Non-Shared Context; File System; Communication and Control; Collaboration Topologies; A2A

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The central question

When Agents work in separate contexts, what structure keeps their work coherent?

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Why this problem matters

Peer loop

A proposer and reviewer iterate with independent evidence.

Manager

One planner decomposes, budgets, schedules, and integrates.

Decentralized

Peers transfer work without a runtime central controller.

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Three ideas to keep in view

Data plane

Workspaces, shared artifacts, external and system mounts

Control plane

Spawn, message, status, terminate, and resource scheduling

Handoff package

Goal + evidence + artifacts + open questions + acceptance

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The book's visual model

Manager architecture for sequential multi-Agent coordination

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Manager topology vs. Decentralized topology

Manager topology

• Global plan
• Simple supervision
• Planner is bottleneck

Decentralized topology

• Local ownership
• Flexible handoffs
• Harder global consistency

Topology determines where planning errors and coordination costs accumulate.

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Pass artifacts; observe lifecycle

job = manager.spawn(role, goal, budget)
manager.message(job, evidence_paths)
while job.running: manager.observe(job.status)
result = manager.verify(job.artifacts)
manager.cancel_dependents_if_satisfied(result)

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Test the claim

10-32 min

Rehearse a four-role translation orchestration

Observe: Manager plan, glossary ownership, chapter budgets, proofreading, and integration

Demo budget: 2 minutes · one contiguous terminal block

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Live demo

Switching to the terminal

$ cd chapter10/book-translation && python demo.py --dry-run

Run the command(s), narrate decisions, and point to the observation—not just the output.

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What the evidence supports

Finding 1

Files handle large persistent artifacts; messages handle asynchronous coordination.

Finding 2

The manager's decomposition quality caps the value of stronger workers.

Finding 3

Explicit status and termination semantics matter as much as task prompts.

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Where the claim stops

Boundary condition

A dry-run proves the orchestration graph and budgets—not translation quality or multi-Agent advantage.

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Engineering takeaway

Design rule

Make ownership, artifact contracts, lifecycle states, budgets, and termination paths explicit before adding Agents.

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Continue the experiment

Experiment 10-3: fixed baseline + autonomous phone registration chapter10/autonomous-phone-registration/ (TalkAct baseline) Agent virtual file system book-en/images/fig10-2.svg Manager parallel coordination book-en/images/fig10-6.svg A2A and decentralized handoffs book-en/chapter10.md

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Pause and apply

Your turn

If the manager decomposes the task incorrectly, which independent gate can detect the mistake before workers waste their budgets?

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Next · Lesson 41

Test whether multiple Agents create new information or merely spend more tokens.

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