Your LLM Is Not an Agent. Your Framework Is Not Enough. You Need a Harness.
The article discusses the challenges faced when deploying AI agents built with large language models (LLMs). It emphasizes that while LLMs are capable, they require a structured operating environment, referred to as a 'Harness,' to function reliably in real-world applications. Without this framework, agents struggle with context retention, memory, and error handling, leading to inconsistent performance and user distrust.
- ▪AI agents often perform well in demos but fail in real-world applications due to lack of structure.
- ▪A 'Harness' is necessary to provide the operating environment that governs how AI agents operate.
- ▪Without a harness, agents can experience context amnesia, infinite loops, and tool failures.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 1829954) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Seenivasa Ramadurai Posted on May 25 Your LLM Is Not an Agent. Your Framework Is Not Enough. You Need a Harness. Introduction Every team building with AI agents hits the same wall. The demo works beautifully. The agent answers questions, calls tools, produces results. Then you ship it and the cracks appear it loses track of what it was doing, burns through API calls in circles, ignores boundaries it should respect, forgets context from five minutes ago. Users lose trust.
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