Five-Layer Framework Explains How AI Agent Harnesses Work Beyond Simple Chat

A technical breakdown published on DEV Community outlines the concept of an AI 'harness' — the infrastructure surrounding a language model that enables it to read files, execute commands, and loop through decisions autonomously. Unlike standard chat interfaces such as ChatGPT web, agentic tools like Cursor, Kiro, Claude Code, and Windsurf qualify as harnesses because they allow the model to act, observe results, and determine next steps without stopping. The framework identifies five sequential layers that underpin effective agent operation: memory, context, skills, agents, and tools, each serving as the foundation for the next. The article warns that skipping layers — such as implementing RAG before organizing a knowledge vault — trades one problem for another. A key insight is that a weaker model with clean, well-structured context will often outperform a more powerful model operating with poor context.