Phase 3: Scale, Failure & Evaluation · ~75 minutes · Python (stdlib)

Agent Economies, Token Incentives, Reputation

Long-horizon autonomous agents (METR's 1-hour to 8-hour work-curve) need economic agency.

Hiring signal: Can operate agent economies, token incentives, reputation in production

Introduction

The emerging 5-layer stack is: DePIN (physical compute) → Identity (W3C DIDs + reputation capital) → Cognition (RAG + MCP) → Settlement (account abstraction) → Governance (Agentic DAOs). Production agent-incentive networks include Bittensor (TAO subnets reward task-specific models), Fetch.ai / ASI Alliance (ASI-1 Mini LLM + FET token), and Gonka (transformer-based PoW that reallocates compute to productive AI tasks). Academic work: AAMAS 2025's decentralized LaMAS uses Shapley-value credit attribution to fairly reward contributing agents; Google Research "Mechanism design for large language models" proposes token auctions with second-price payment under monotone aggregation. This lesson builds a minimal agent marketplace, applies Shapley-value credit attribution to a multi-agent pipeline, and runs a second-price token auction so the game-theory machinery lands concretely.

Type: Learn Languages: Python (stdlib) Prerequisites: Phase 16 · 16 (Negotiation and Bargaining), Phase 16 · 09 (Parallel Swarm Networks) Time: ~75 minutes

Objective

Learning objectives

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You've read the first 2 sections. The rest of this lesson covers The Problem, The Concept, Build, Check Yourself, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.

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