{"id":40825,"date":"2026-09-27T07:12:16","date_gmt":"2026-09-27T00:12:16","guid":{"rendered":"https:\/\/xbookvn.com\/self-prompting-ai-system-building-autonomous-claude-agents\/"},"modified":"2026-09-27T07:12:16","modified_gmt":"2026-09-27T00:12:16","slug":"self-prompting-ai-system-building-autonomous-claude-agents","status":"publish","type":"post","link":"https:\/\/xbookvn.com\/en\/self-prompting-ai-system-building-autonomous-claude-agents\/","title":{"rendered":"self-prompting AI system: Building Autonomous Claude Agents"},"content":{"rendered":"<div class=\"seo-article-body\" style=\"width:100%;max-width:830px;margin:0 auto;line-height:1.7\">\n<p>The concept of a <strong>self-prompting AI system<\/strong> is reshaping how developers interact with large language models like Claude. Rather than manually crafting prompts for each task, the goal is to let the agent generate and refine its own prompts, leading to more autonomous and efficient workflows.<\/p>\n<p>In a presentation held a little over two months ago, Anthropic engineer Lamis Mukta detailed the evolution of AI agent memory. She explained how agents moved from remembering only within a single session to adopting <strong>persistent memory<\/strong> that spans multiple interactions. &quot;Trong bu\u1ed5i chia s\u1ebb c\u00e1ch \u0111\u00e2y h\u01a1n 2 th\u00e1ng, Lamis Mukta &#8211; k\u1ef9 s\u01b0 t\u1ea1i Anthropic &#8211; \u0111\u00e3 tr\u00ecnh b\u00e0y v\u1ec1 h\u00e0nh tr\u00ecnh ph\u00e1t tri\u1ec3n c\u1ee7a b\u1ed9 nh\u1edb AI agent: t\u1eeb vi\u1ec7c ch\u1ec9 nh\u1edb trong ph\u1ea1m vi m\u1ed9t session, \u0111\u1ebfn vi\u1ec7c s\u1edf h\u1eefu b\u1ed9 nh\u1edb d\u00e0i h\u1ea1n (persistent memory).&quot;<\/p>\n<p>One challenge she highlighted is the limitation of <strong>in-band memory<\/strong> &#8211; the ability to retain context directly within a conversation. &quot;V\u00ec sao \\&quot;in-band memory\\&quot; (nh\u1edb ngay trong h\u1ed9i tho\u1ea1i) s\u1edbm mu\u1ed9n c\u0169ng ch\u1ea1m gi\u1edbi h\u1ea1n, v\u00e0 khi\u1ebfn agent m\u1ea5t t\u1eadp trung khi h\u1ec7 th\u1ed1ng l\u1edbn d\u1ea7n&quot;. As agents scale, this approach can cause focus drift and degrade performance.<\/p>\n<p>To overcome these constraints, Mukta introduced a <strong>&quot;dreaming&quot;<\/strong> mechanism. This allows the agent to review prior sessions, learn from mistakes, and improve over successive runs. &quot;C\u01a1 ch\u1ebf \\&quot;dreaming\\&quot; &#8211; cho ph\u00e9p agent t\u1ef1 xem l\u1ea1i phi\u00ean l\u00e0m vi\u1ec7c tr\u01b0\u1edbc \u0111\u00f3, r\u00fat kinh nghi\u1ec7m t\u1eeb l\u1ed7i sai, v\u00e0 th\u00f4ng minh h\u01a1n qua t\u1eebng l\u1ea7n ch\u1ea1y&quot;. Implementing such capabilities in production demands robust guardrails, including versioning and concurrency handling, to ensure the system self-optimizes without breaking. &quot;Nh\u1eefng guardrail c\u1ea7n c\u00f3 khi \u0111\u01b0a v\u00e0o production (versioning, x\u1eed l\u00fd concurrency&#8230;) \u0111\u1ec3 h\u1ec7 th\u1ed1ng t\u1ef1 c\u1ea3i thi\u1ec7n kh\u00f4ng b\u1ecb v\u1ee1 tr\u1eadn&quot;<\/p>\n<p>The overarching message from the Anthropic engineer was clear: &quot;B\u1ea1n c\u1ea7n x\u00e2y m\u1ed9t h\u1ec7 th\u1ed1ng t\u1ef1 prompt ch\u00ednh n\u00f3\\&quot;&quot;. As she put it, &quot;K\u1ef9 s\u01b0 c\u1ee7a Anthropic: \\&quot;B\u1ea1n kh\u00f4ng c\u1ea7n prompt Claude.&quot; By adopting a self-prompting architecture, teams can shift from manual prompt engineering to building agents that manage and evolve their own prompting logic.<\/p>\n<h2>Conclusion<\/h2>\n<p>Embracing a self-prompting AI system transforms Claude agents from static responders into dynamic, self-optimizing collaborators. With persistent memory, dreaming cycles, and production-grade guardrails, developers can reduce manual effort while scaling AI capabilities responsibly.<\/p>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>self-prompting AI system enables Claude agents to manage their own prompts, reducing manual effort and improving scalability.<\/p>","protected":false},"author":45105,"featured_media":40824,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_yoast_wpseo_linkdex":"90","_yoast_wpseo_content_score":"90","pmpro_default_level":"","rank_math_focus_keyword":"","rank_math_description":"","rank_math_title":"","source_url":"","source_name":"AI-Staff-Xbook-Blog","footnotes":""},"categories":[237],"tags":[],"class_list":["post-40825","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog","pmpro-has-access"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.9 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>self-prompting AI system: Building Autonomous Claude Agents - 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