Your AI agent is only as good as the environment you build around it. Prompt engineering got us started. Context engineering leveled us up. But as AI agents become autonomous - writing code, running tests, and deploying changes - a new discipline has emerged: Harness Engineering. Harness Engineering for Web Developers is a practical guide that walks you through the three-stage evolution of working with AI, from crafting single prompts to designing full autonomous agent environments. What you will learn: Prompt engineering fundamentals - role prompting, chain of thought, few-shot patternsContext engineering - context windows, RAG, tool use, MCP, and memory systemsHarness engineering - architecture constraints, garbage collection, and reliability mathCLAUDE.md and AGENTS.md - writing effective agent document mapsThe Generator/Evaluator pattern - separating work and verification for reliable outputBuilding a harness step by step - linters, pre-commit hooks, ArchUnit, CI/CD gatesProduction harness operations - monitoring, cost management, and self-improving systemsIncludes code recipes for Claude Code, Cursor, GitHub Copilot, and OpenAI Codex. Every concept is illustrated with real-world examples using Spring Boot, FastAPI, React, and TypeScript. Why 95% accuracy is not enough: A 20-step agent pipeline at 95% per step succeeds only 36% of the time. This book shows you how to build the harness that turns that 36% into 95%+. Written for developers who want to move from talking to AI to building systems where AI works autonomously and reliably.
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