{"id":251533,"date":"2026-05-21T12:08:00","date_gmt":"2026-05-21T16:08:00","guid":{"rendered":"https:\/\/news-you-need.com\/index.php\/2026\/05\/21\/break-the-context-window-barrier-with-amazon-bedrock-agentcore\/"},"modified":"2026-05-21T12:20:09","modified_gmt":"2026-05-21T16:20:09","slug":"break-the-context-window-barrier-with-amazon-bedrock-agentcore","status":"publish","type":"post","link":"https:\/\/news-you-need.com\/index.php\/2026\/05\/21\/break-the-context-window-barrier-with-amazon-bedrock-agentcore\/","title":{"rendered":"Break the context window barrier with Amazon Bedrock AgentCore"},"content":{"rendered":"<p><a href=\"https:\/\/aws.amazon.com\/blogs\/machine-learning\/break-the-context-window-barrier-with-amazon-bedrock-agentcore\/\">Break the context window barrier with Amazon Bedrock AgentCore<\/a><\/p>\n<p><a href=\"https:\/\/aws.amazon.com\/blogs\/machine-learning\/break-the-context-window-barrier-with-amazon-bedrock-agentcore\/\">https:\/\/aws.amazon.com\/blogs\/machine-learning\/break-the-context-window-barrier-with-amazon-bedrock-agentcore\/<\/a><\/p>\n<p>Publish Date: <a href=\"publish_date]\">2026-05-21 12:08:00<\/a><\/p>\n<p>Source Domain: <a href=\"aws.amazon.com\">aws.amazon.com<\/a><\/p>\n<p>When you analyze documents that span millions of characters, you hit the context window barrier and even the largest context windows fall short. Your model either rejects the input or produces answers based on incomplete information. How do you reason over documents that don\u2019t fit?<\/p>\n<p>In this post, you will learn how to implement Recursive Language Models (RLM) using Amazon Bedrock AgentCore Code Interpreter and the Strands Agents SDK. By the end, you will know how to:<\/p>\n<ul>\n<li>Process documents of varying lengths, with no upper bound on context size.<\/li>\n<li>Use Bedrock AgentCore Code Interpreter as persistent working memory for iterative document analysis.<\/li>\n<li>Orchestrate sub-large language model (sub-LLM) calls from within a sandboxed Python environment to analyze specific document sections.<\/li>\n<\/ul>\n<h2>Why context windows aren\u2019t enough<\/h2>\n<p>Consider a typical financial analysis task of comparing metrics across two years of annual reports from a single company. Each report runs 300\u2013500 pages. Add analyst reports, SEC filings, and supplementary materials, and the total reaches millions of characters.<\/p>\n<p>When you send these documents directly to a model, either the input exceeds the model\u2019s context window limit and the request fails, or the input fits but the model has difficulty attending to information in the middle of long inputs, often referred to as the \u201clost in the middle\u201d problem.<\/p>\n<p>Both failure modes exist because context window size is a hard limit that prompt engineering alone can\u2019t solve. You need an approach that decouples document size from the model\u2019s context window.<\/p>\n<h2>RLMs: Treating context as an environment<\/h2>\n<p>RLMs, introduced by Zhang et al. in arXiv:2512.24601, reframe the problem. Instead of feeding an entire document into the model\u2019s context window, an RLM treats the input as an external environment that the model interacts with programmatically.<\/p>\n<\/p>\n<p>Figure 1&#8230;.<\/p>\n<p><a href=\"https:\/\/aws.amazon.com\/blogs\/machine-learning\/break-the-context-window-barrier-with-amazon-bedrock-agentcore\/\">Source<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Break the context window barrier with Amazon Bedrock AgentCore https:\/\/aws.amazon.com\/blogs\/machine-learning\/break-the-context-window-barrier-with-amazon-bedrock-agentcore\/ Publish Date: 2026-05-21 12:08:00 Source&#8230;<\/p>\n","protected":false},"author":1,"featured_media":251535,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"fifu_image_url":"https:\/\/d2908q01vomqb2.cloudfront.net\/f1f836cb4ea6efb2a0b1b99f41ad8b103eff4b59\/2026\/05\/21\/20487.png","fifu_image_alt":"","footnotes":""},"categories":[14],"tags":[18,17],"class_list":["post-251533","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-artificial-intelligence","tag-large-language-model","tag-llm"],"_links":{"self":[{"href":"https:\/\/news-you-need.com\/index.php\/wp-json\/wp\/v2\/posts\/251533"}],"collection":[{"href":"https:\/\/news-you-need.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/news-you-need.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/news-you-need.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/news-you-need.com\/index.php\/wp-json\/wp\/v2\/comments?post=251533"}],"version-history":[{"count":1,"href":"https:\/\/news-you-need.com\/index.php\/wp-json\/wp\/v2\/posts\/251533\/revisions"}],"predecessor-version":[{"id":251537,"href":"https:\/\/news-you-need.com\/index.php\/wp-json\/wp\/v2\/posts\/251533\/revisions\/251537"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/news-you-need.com\/index.php\/wp-json\/wp\/v2\/media\/251535"}],"wp:attachment":[{"href":"https:\/\/news-you-need.com\/index.php\/wp-json\/wp\/v2\/media?parent=251533"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/news-you-need.com\/index.php\/wp-json\/wp\/v2\/categories?post=251533"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/news-you-need.com\/index.php\/wp-json\/wp\/v2\/tags?post=251533"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}