Harness Engineering: the 5 layers of the agent - memory, context, skills, agents and tools
Imagina um piloto de Fórmula 1 sentado num banco de praça. Ele sabe pilotar. Mas sem o carro, sem o volante, sem a experiência, sem o box ele não corre. O modelo de IA é o piloto. O harness é o carro inteiro. Cursor, Kiro, Claude Code, Windsurf, Cline. Essas não são "IDEs com chat". São harnesses — ambientes completos onde o modelo opera em loop, lê arquivos, executa comandos, e decide o próximo…
The article discusses the concept of a "harness" in artificial intelligence, which refers to the infrastructure that supports an AI model, enabling it to operate effectively. A harness is not just a chat interface, but a complete environment that allows the model to read files, execute commands, and decide the next step. The article highlights the differences between a harness and a simple chat interface, using examples such as Cursor, Kiro, and Claude Code.
It also outlines a five-layer framework for building a harness, consisting of memory, context, skills, agents, and tools, and explains the importance of each layer in enabling the AI model to operate efficiently.
Written by urgent.news from Dev.to's report — not a translation of it. Machine-written — may contain errors; check the original before relying on it.
