Break the context window barrier with Amazon Bedrock AgentCore

2026-05-21 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Advanced, long

Summary

Recursive Language Models (RLM) implemented with Amazon Bedrock AgentCore Code Interpreter and Strands Agents SDK offer a solution to overcome LLM context window limitations for analyzing extremely long documents. This architecture employs a root LLM to orchestrate analysis by writing Python code within a sandboxed environment, delegating semantic tasks to sub-LLMs, and accumulating results in persistent working memory. This approach enables processing documents spanning millions of characters, far exceeding typical context windows. Evaluations on LongBench v2 Financial Multi-Document QA and Code Repository Understanding datasets demonstrate RLM's effectiveness, achieving a 100% success rate and significantly improving accuracy. For example, Claude Opus 4.6 with RLM reached 80.0% accuracy on Financial QA, compared to 66.7% for Long Context, and Claude Sonnet 4.5 with RLM achieved 76.0% on Code Repository QA, up from 46.0% for Long Context.

Key takeaway

For AI Engineers building solutions for extensive document analysis, you should consider implementing Recursive Language Models (RLM) with Amazon Bedrock AgentCore Code Interpreter. This approach allows you to overcome context window limitations, achieving 100% success rates and significantly higher accuracy on tasks like financial or code repository understanding. You can process documents spanning millions of characters by orchestrating sub-LLM calls within a sandboxed Python environment, maintaining persistent working memory. Be mindful of increased latency and cost, optimizing with smaller sub-LLMs for efficiency.

Key insights

RLM treats context as an external environment, enabling LLMs to programmatically interact with and analyze arbitrarily long documents.

Principles

• Decouple document size from LLM context window.
• Orchestrate analysis via code in a sandboxed environment.
• Maintain persistent working memory for iterative tasks.

Method

Implement RLM using Amazon Bedrock AgentCore Code Interpreter as a sandboxed Python runtime with persistent state. A root LLM generates code to explore documents, calling sub-LLMs via an injected "llm_query()" function for semantic analysis, keeping results in Python variables.

In practice

• Analyze financial reports and SEC filings.
• Navigate and understand large codebases.
• Optimize cost using smaller sub-LLMs like Haiku 4.5.

Topics

• Recursive Language Models
• Amazon Bedrock AgentCore
• LLM Context Windows
• Code Interpreter
• Long-Context QA
• Strands Agents SDK

Best for: AI Engineer, Machine Learning Engineer, AI Scientist

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• Claude Code's Hidden Multi-Agent Orchestration now Open-source — AI agents are evolving to manage complex tasks and context more autonomously and efficiently.
• LLM with 12M Context Window — SubQ introduces subquadratic sparse attention, enabling massive context windows with significantly reduced compute and cost.
• alexzhang13 / rlm — RLMs enable LMs to programmatically self-call and decompose tasks for near-infinite context handling.
• Recursive Language Models: An All-in-One Deep Dive — RLMs use a REPL and pass-by-reference to enable LLMs to handle long contexts and complex tasks efficiently.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.