A11 as a Cognitive Layer for Autonomous Agents in Isolated Execution Environments
The article discusses the architecture of autonomous agents that operate within isolated execution environments and introduces the A11 cognitive control layer. It highlights the limitations of current LLM-based agents and outlines their core failure modes. The A11 specification is presented as a solution to stabilize these agents by addressing their persistent goal representation and integration issues.
- ▪The article formalizes the architecture of autonomous LLM-based agents in isolated execution environments.
- ▪It identifies core failure modes such as goal drift and context collapse that affect these agents.
- ▪The A11 cognitive layer is proposed to enhance the stability and functionality of autonomous agents.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3703831) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Алексей Гормен Posted on May 19 A11 as a Cognitive Layer for Autonomous Agents in Isolated Execution Environments #ai #agents #security #machinelearning 1. Purpose This article formalizes: the architecture of autonomous LLM‑based agents running inside isolated execution environments their fundamental limitations the need for a cognitive control layer the A11 specification as such a layer a complete JSON structure that models can use as an operational template The article is written…
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