The Answer to the Harness Question (2 minute read)

TLDR AI News

Summary

The article argues that AI harnesses serve two distinct purposes—providing context about what the user wants (intent) and instructions on how to achieve it (execution)—and that these age differently: execution instructions become less valuable as models improve, while intent context becomes more valuable.

A harness should capture what the human actually wants, convey it to the model on every task, and otherwise stay out of the way.
Original Article
View Cached Full Text

Cached at: 07/30/26, 06:26 PM

# The Answer to the Harness Question Source: [https://danielmiessler.com/blog/the-answer-to-the-harness-question](https://danielmiessler.com/blog/the-answer-to-the-harness-question) Harnesses are for intent; models are for execution July 29, 2026 [![The Answer to the Harness Question](https://danielmiessler.com/images/the-answer-to-the-harness-question.webp)](https://danielmiessler.com/images/the-answer-to-the-harness-question.webp) Martin Casado posted something about AI harnesses that captures where a lot of smart people are stuck right now\. > On harnesses, I vacillate between three beliefs: the less harness, the better\. Models are the magic\. Post training a model and harness is dramatically better and the model providers win\. Harnesses have real independent value from the model\. I have no idea which is right\.[Martin Casado](https://x.com/martin_casado/status/2082527395920347362) I think I can answer this\. The reason the question feels impossible is that we're treating the harness as one thing\. It's actually two\. Every harness carries some mix of WHAT and HOW—context about what you want, and instructions for how to get it\. And those two halves age in opposite directions\. The HOW half rots\. This is[Sutton's Bitter Lesson](http://www.incompleteideas.net/IncIdeas/BitterLesson.html)playing out in your config files: the smarter models get, the dumber your step\-by\-step instructions look by comparison\. If your harness is mostly HOW, then Martin's first belief is correct\. Less harness is better, because the model is the magic\. The WHAT half appreciates\. Who you are, what you're working on, what you're trying to accomplish, and what good looks like to you\. A smarter model does more with that context, not less\. If your harness is mostly WHAT, then his third belief is correct\. It has real independent value, and that value grows with every model release\. So beliefs one and three are both right\. They're just about different halves of the harness\. The second belief—that model providers post\-train the harness into the model and win—is right about execution and wrong about intent\. The labs can absolutely train models to be better agents, and they will\. But they can't post\-train YOUR context into the model\. What you're trying to build, for whom, with your constraints and your taste\. That has to be captured and conveyed from outside, every single time\. That's what the harness is for\. I've been calling this[Intent Engineering](https://danielmiessler.com/blog/intent-engineering), and it's the whole design principle behind[my own harness](https://danielmiessler.com/blog/personal-ai-infrastructure): capture what the human actually wants, convey it to the model on every task, and otherwise stay out of the way\. So YES to harness\. Extremely powerful\. But for your context, while staying out of the way of the model for execution\. #### Notes For roughly 29\.7701 years I've written here, ad\-free—3,075 essays and tutorials and counting\. If it's useful to you, a monthly or one\-time donation keeps it going\. 🫶🏼 ### Monthly ### One\-Time

Similar Articles

@sairahul1: https://x.com/sairahul1/status/2063544956158185927

X AI KOLs Timeline

This article introduces the concept of 'Harness Engineering,' a discipline focused on designing the systems that constrain and guide AI agents to make them reliable in production, arguing that the harness matters more than the model itself.

Own the Loop: A Field Guide to Agent Harnesses (5 minute read)

TLDR AI

As AI coding models become commoditized, the agent harness—the control loop managing tools and workflows—emerges as the key differentiator. This guide maps the field of harnesses, weighing vendor-native performance against the portability of model-agnostic workflows.