Build context-rich research agents with Deep Agents and Bedrock AgentCore — 2026-06-15

## Short Segments GLM-5.2 from Z.ai introduces a groundbreaking 1-million-token context window, redefining coding agent capabilities. Today, we'll explore how this massive context window changes the game for developers, and later, we'll dive into building context-rich research agents with Deep Agents and Bedrock AgentCore. But first, let's look at how Claude Code is evolving with 25 new features and strategies. Claude Code Guide 2026 reveals 25 features with examples and demos, showcasing its evolution from a terminal coding assistant to a layered agentic system. Anthropic's Claude Code now operates with distinct layers for memory, hooks, skills, subagents, plugins, and MCP, enhancing its capabilities significantly. This guide is tailored for AI engineers, software engineers, and data scientists, providing documented code examples and labeling each feature by status. Claude Code's agentic loop allows it to read files, run commands, edit code, and call external tools, making it a versatile tool for developers. Safety is ensured through permission modes, checkpoints, sandboxing, and managed settings, while developers can extend its functionality using primitives like CLAUDE.md, skills, and subagents. With these enhancements, Claude Code is set to redefine AI-assisted development at scale. ## Feature Story Building context-rich research agents is now more efficient with Deep Agents and Bedrock AgentCore. In AI-powered research workflows, balancing depth and context has been a persistent challenge. When an agent processes multiple web pages or runs data analysis, its context window can quickly become overwhelmed, leading to inefficiencies. Traditionally, teams have managed this with manual prompt-chaining or sequential processing, but a more effective solution is now available. LangChain Deep Agents orchestrates the delegation of deep work to isolated subagents, which return concise results, optimizing the workflow. Amazon Bedrock AgentCore provides the necessary infrastructure for these subagents, including a real browser in a MicroVM for web research and a full Python environment for data analysis. This setup allows developers to build competitive research agents that can handle complex, multi-step AI workflows with isolated execution environments. By deploying these agents to the Bedrock AgentCore Runtime using the AgentCore CLI, developers can streamline their research processes significantly. This development is particularly beneficial for those building AI workflows that require extensive research, validation, reasoning, and synthesis. As AI continues to evolve, tools like Deep Agents and Bedrock AgentCore are crucial for enhancing the efficiency and effectiveness of research agents.