Agent Harness Engineering

Agent harness engineering is a discipline focused on designing the components around an AI model to transform it into a functional agent, emphasizing the importance of prompts, tools, runtime, hooks, credentials, and observability. The discipline is guided by two principles: making every observed mistake a permanent constraint and ensuring each component encodes a specific assumption about the model's capabilities. This approach addresses the common failure points at boundary components like credential handling, fallback paths, and tool descriptions, rather than the model-facing components. Harness engineering emerged as significant in late 2025, as it became evident that the surrounding harness, rather than the model itself, often defines the success of production agents, with Vivek Trivedy’s work exemplifying how changing only the harness dramatically improved agent performance. The field highlights the need for a platform team to manage the harness for non-developer users, ensuring reliability and security through practices like tool curation and vault-backed credentials, particularly in enterprise contexts where users cannot maintain the harness themselves. Lunar’s MCPX gateway is an example of a system that enforces harness engineering at the protocol layer, supporting enterprise AI adoption beyond engineering departments by providing a stable, hardened surface managed by those who can read and adjust the harness components.