Phase 2: Coordination & Agreement · ~75 minutes · Python (stdlib)
Negotiation and Bargaining
Agents negotiate resources, prices, task allocations, and terms.
Hiring signal: Can operate negotiation and bargaining in production
Introduction
The 2026 benchmark set is clear: NegotiationArena (arXiv:2402.05863) shows LLMs can improve payoffs ~20% via persona manipulation ("desperation"); "Measuring Bargaining Abilities" (arXiv:2402.15813) shows buyer is harder than seller and scale does not help — their OG-Narrator (deterministic offer generator + LLM narrator) pushed deal rate from 26.67% to 88.88%; the Large-Scale Autonomous Negotiation Competition (arXiv:2503.06416) ran ~180k negotiations and found that chain-of-thought-concealing agents win by hiding reasoning from counterparts; Bhattacharya et al. 2025 on Harvard Negotiation Project metrics ranked Llama-3 most-effective, Claude-3 aggressive, GPT-4 fairest. This lesson implements Contract Net Protocol (the FIPA ancestor, Lesson 02), wires an LLM-style buyer/seller, runs an OG-Narrator-style decomposition, and measures how deal rate changes with each structural choice.
Type: Learn + Build Languages: Python (stdlib) Prerequisites: Phase 16 · 02 (FIPA-ACL Heritage), Phase 16 · 09 (Parallel Swarm Networks) Time: ~75 minutes
Objective
Learning objectives
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