@snwiki238337: I've noticed that many people seem to confuse Agent with Harness. Here's a formula to help you understand: Agent = Harness + Model
Summary
This article clarifies the confusion between Agent and Harness in the AI field, proposing the formula Agent = Harness + Model to aid understanding.
Similar Articles
@shao__meng: Updated lecture notes for MIT OpenCourseWare 6.S950 Agency with AI, Lecture 5: The Harness — https://agencyai.mit.edu/w5/ Covering observability, controllability, and "how much Harness is enough" through a 300-line teaching Agent...
Updated lecture notes for MIT OpenCourseWare 6.S950 Agency with AI, Lecture 5: The Harness, walking through a ~300-line teaching Agent to explain the principles of observability, controllability, and the question of how much Harness you actually need.
@Potatoloogs: https://x.com/Potatoloogs/status/2057391224592667051
This article deeply analyzes the concept of Agent Harness, which is the engineering infrastructure wrapped around an LLM, including 12 components such as orchestration loops, tool calling, memory systems, context management, etc. The article cites practices from companies like Anthropic, OpenAI, and LangChain, arguing for the critical role of the harness in production-grade AI agents.
@Xudong07452910: This 'Harness Updating Is Not Harness Benefit' is very suitable for those working on Agent Harness. It talks about an easily overlooked problem: updating Harness does not mean you can use it well. Now many Ag…
This post discusses a paper, pointing out that in the self-evolution of Agent systems, updating Harness (writing useful updates) and benefiting from updates (actually using them in subsequent tasks) are two different abilities. The latter is key, and weak models often fail to use the rules.
@Saboo_Shubham_: wtf is an agent harness. An agent is a model plus a harness. Model reads the context and picks the next action. The har…
A breakdown of what an 'agent harness' is — the code layer around a model that turns model decisions into real work — detailing 8 common components: the loop, tools/sandbox, subagents, context management, memory, guardrails, verification, and observability.
What Is a Harness?
The article explains the concept of AI agent harnesses by comparing them to climbing harnesses, detailing components like system prompts and tools that enable AI models to function as agents.