ai-agent-book/slides/lesson-42.md

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theme	title	info	author	transition	mdc	lineNumbers	monaco	aspectRatio	canvasWidth	layout	class
seriph	Lesson 42 — How Do Agent Teams Fail—and What Should We Build Next?	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

How Do Agent Teams Fail—and What Should We Build Next?

Conflicts, error cascades, Agent societies, and the course synthesis

Lesson 42 of 42 · 18 minutes · Failure Modes; Agent Society; Economic Competition; Strategic Gameplay

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The central question

How do we prevent local mistakes from becoming group failures while still allowing collective behavior to emerge?

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Why this problem matters

Concurrency

Shared files can lose updates or encode semantic conflicts.

Cascades

A wrong upstream claim is amplified by trusting downstream Agents.

Emergence

Persistent Agents produce social, strategic, and economic behavior not explicitly scripted.

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Three ideas to keep in view

Optimistic locking

Detect version change before committing a shared write

Information control

A code judge reveals only what each role may know

External reward

Society outcomes are scored by the environment—not self-report

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The book's visual model

Voice Werewolf multi-Agent system

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Agent chat room vs. Governed society

Agent chat room

• Everyone sees everything
• Loose role prompts
• Claims spread unchecked

Governed society

• State authority in code
• Role-scoped views
• Auditable actions and rewards

Social complexity requires stronger state and information governance.

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The judge owns truth and disclosure

private_view = judge.view_for(player, global_state)
action = player.act(private_view)
judge.validate(action, role=player.role)
global_state = judge.apply(action)
audit.append(player.id, action, state_hash(global_state))

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Test the claim

10-8 offline diagnostic2 min

Run a deterministic information-isolation game

Observe: Private role context, phase transitions, legal actions, votes, and winner gates

Demo budget: 2 minutes · one contiguous terminal block

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Live demo

Switching to the terminal

$ cd chapter10/voice-werewolf && python demo.py --offline

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

Coordination failures are distributed-systems failures plus probabilistic decision errors.

Finding 2

A code-driven authority can preserve information asymmetry and rule integrity.

Finding 3

Social and economic simulations can generate new experience—but also collusion and pathology.

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Where the claim stops

Boundary condition

An offline all-AI diagnostic does not satisfy the book's live human voice acceptance criteria.

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Engineering takeaway

Design rule

Keep shared truth, permissions, conflict detection, and final rewards outside the Agents that compete or collaborate.

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Continue the experiment

Experiment 10-7: Stanford Generative Agents https://github.com/joonspk-research/generative_agents Experiment 10-8: consent-gated voice path chapter10/voice-werewolf/README.md AI Town architecture book-en/images/fig10-10.svg Agent society and economy cases book-en/chapter10.md

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The complete course arc

Build · Lessons 01–21

Context → memory → tools → code

Improve · Lessons 22–34

Evaluation → training → continual evolution

Expand · Lessons 35–42

Voice → embodied action → collaboration

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Pause and apply

Your turn

Which group-level failure cannot be prevented by improving any single Agent in isolation?

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Course synthesis

1Define the failure

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2Run a controlled experiment

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3Interpret the evidence

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4Update safely

↺ Repeat when new evidence arrives