Without a harness, a model can answer questions, but it can’t reliably run code, call APIs, access files, remember prior work or complete multi-step workflows on its own.

In this guide, we’ll cover the core components of an AI agent harness, why harnesses shape agent performance, how production agent systems are built and why harness engineering is emerging as its own discipline.

AI agents rely on two complementary layers: a model that reasons and a harness that acts.

The model, whether GPT-5.5, Claude, Llama or another LLM, reads context and decides what to do next. The harness turns those decisions into actions by connecting the model to tools, memory and external systems.