Phase 2: Coordination & Agreement · ~75 minutes · Python (stdlib)

Generative Agents and Emergent Simulation

Park et al.

Hiring signal: Can operate generative agents and emergent simulation in production

Introduction

2023 (UIST '23, arXiv:2304.03442) populated Smallville, a sandbox of 25 agents, with a three-part architecture: memory stream (natural-language log), reflection (higher-level syntheses the agent generates about its own stream), and plan (day-level behavior, then sub-plans). The landmark result was the Valentine's Day party emergence: one agent seeded with "wants to throw a Valentine's Day party," without further scripting, produced invitations spread through the population, coordinated dates, and the party happened — from 24 agents who started with no knowledge of it. Ablations show all three components are required for believability. The documented failures are spatial-norm errors (entering closed stores, sharing single-person bathrooms). This is the reference architecture for agent simulations and multi-agent social evaluation in 2026.

Type: Learn + Build Languages: Python (stdlib) Prerequisites: Phase 16 · 04 (Primitive Model), Phase 16 · 13 (Shared Memory) Time: ~75 minutes

Objective

Learning objectives

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Key terms in this lesson

Agent