The architecture surrounding LLMs has evolved from simple chat interfaces and basic rule files into a comprehensive technological layer known as a harness. This layer now functions as a distinct infrastructure, establishing its own protocols and standards while shifting vendor focus away from individual models.
A harness serves as the infrastructure that transforms a stateless LLM into an active system. It provides essential capabilities, including access to external tools, context management for memory and sessions, behavioral rules, and security protocols. It also supports advanced features like orchestration, hooks, and specialized skills.
The concept of an AI agent is defined as the combination of a model and a harness. While the model acts as a semantic brain, the harness serves as the body, enabling the agent to perceive environmental states, maintain context across multiple steps, and execute complex, multi-stage tasks.
