In a recent LinkedIn post, Itay Hacmon discusses a novel approach to overcoming the limitations of Artificial Intelligence models, particularly their tendency to repeat errors. Hacmon explains that instead of relying on different AI models, he developed a surrounding system, which he calls ‘Harness,’ composed of six distinct layers designed to guide and improve AI performance.
Hacmon details his initial frustrations with AI’s inability to fully comprehend his instructions. However, a shift in his methodology led to significant improvements. He adopted a strategy of actively addressing each instance where the AI faltered.
“Every time it failed, I did something with it. And today? I no longer guide it myself.”
This iterative feedback loop, Hacmon explains, was crucial. It led to the development of a system that guides the AI in his stead, effectively creating an intelligent wrapper around the core AI model.
The Six Layers of ‘Harness’
The ‘Harness’ system, as described by Hacmon, consists of six layers, each serving as a bridge over the AI’s inherent limitations. These layers are:
- Rules: Establishing predefined guidelines and constraints for the AI’s operation.
- Tools: Providing the AI with access to external resources or functionalities it lacks natively.
- Boundaries: Setting clear limits to prevent the AI from deviating into undesirable or incorrect behaviors.
- Tests: Implementing verification mechanisms to check the AI’s outputs and performance.
- Agents: Delegating specific tasks or decision-making processes to specialized AI components.
- Loop: Creating a continuous cycle of execution, evaluation, and refinement.
Hacmon emphasizes that this structured approach allows the AI to perform tasks it might otherwise struggle with, by providing it with the necessary context, support, and validation.
“Everything that surrounds the model: rules, tools, boundaries, and tests. Each layer is a bridge over something the model cannot do.”
Building and Implementing the System
Hacmon shared that he broke down these six layers in a carousel post, offering a visual and detailed explanation of each component’s function. This pedagogical approach aims to demystify the process of building such an AI enhancement system.
For those interested in a deeper understanding and practical application, Hacmon offers further guidance within his WhatsApp community. He indicates that in this community, he breaks down precisely how to construct this system.
“For those who want to delve deeper, in my WhatsApp community I break down exactly how to build this.”
By sharing these insights, Itay Hacmon encourages broader adoption of more robust AI interaction methodologies. He calls on his network to share the post to help others close the performance gap with AI systems.
“Share to help more people close this gap.”
This initiative by Hacmon highlights a growing trend in the AI field: moving beyond simply using raw models to building sophisticated frameworks that maximize their utility and reliability.
📝 About This Content
This article is based on insights shared by Itay Hacmon on LinkedIn.
📅 Originally posted on July 27, 2026 | View original post on LinkedIn →