AI Engineering/Phase 14/Lesson 25

Multi-Agent Debate and Collaboration

Du et al. (ICML 2024, "Society of Minds") run N model instances that independently propose answers, then iteratively critique each other over R rounds to converge. Improves factuality, rule-following, reasoning. Sparse topology beats full mesh on token cost.

Learn + Build 1 hr Python (stdlib)

Prerequisites: Phase 14 · 12 (Workflow Patterns), Phase 14 · 05 (Self-Refine and CRITIC)

#Learning Objectives

Explain the debate protocol: N proposers, R rounds, converge on a shared answer.
Describe why debate improves factuality, rule-following, and reasoning.
Explain sparse topology: not every debater needs to see every other.
Implement a stdlib debate over a scripted LLM with full-mesh and sparse variants; measure token cost vs accuracy.

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The text and code of this lesson are based on AI Engineering from Scratch by Rohit Ghumare, MIT licensed.