Harness Engineering: The Unglamorous Work That Makes AI Agents Work
Harness engineering is a crucial but often overlooked aspect of developing AI agents. It encompasses the infrastructure that supports the execution, evaluation, observability, safety, and memory of these systems. While the AI model garners most of the attention, the real challenges and innovations lie in the scaffolding that surrounds it.
- ▪Harness engineering focuses on the scaffolding necessary for AI agents to function effectively.
- ▪It includes execution orchestration, evaluation, observability, safety guardrails, and memory management.
- ▪Teams that prioritize harness engineering are more likely to deliver reliable products than those that treat it as an afterthought.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 1850682) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Syed Mohammed Faham Posted on May 20 Harness Engineering: The Unglamorous Work That Makes AI Agents Work #agents #harness #ai #systemdesign TLDR: Everyone obsesses over which AI model to use. But in production agent systems, the model is rarely the bottleneck - the scaffolding around it is. Harness engineering is the discipline of building that scaffolding: execution orchestration, evaluation, observability, safety guardrails, and memory.
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