@omarsar0: Build and own your harness, folks. Very few people understand the magic behind customizing and optimizing an agent harn…
Summary
The tweet emphasizes building custom AI agent harnesses to optimize performance, citing Pi's adoption and discussing self-improving algorithms and local models for better control and efficiency.
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Cached at: 09/13/26, 11:18 PM
Build and own your harness, folks.
Very few people understand the magic behind customizing and optimizing an agent harness for your work.
You can start seeing incredible results quickly, even with a minimal harness. Just better code, better outputs, better costs, better writing, and so on.
As an example, I see way too many people complain that they are tired of their out-of-the-box agent harness producing verbose outputs. If you built a harness, that’s easier to fix with a system prompt optimized for you.
This is why Pi is getting insane adoption these days. You want to control your harness. I agree that it’s hard to optimize where things stand, but self-improving algorithms will make it easier to learn from trajectories. And if you use local models, you can tune all of that end-to-end (through a co-evolution of the harness and the models) as you work with them.
Look into open-source projects like Eve, Exo, and Prime Agent, and you will see what I mean.
Uncle Bob Martin (@unclebobmartin): OK. It’s time to rethink this.
I’ve spend the last several weeks working on a harness that tightly constrains the agents to work the way that I want them to work. I set up all kinds of gates, and tests, and tools, and protocols, and …
And while I was heads-down getting
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