Build context-rich research agents with Deep Agents and Bedrock AgentCore

📰 AWS Machine Learning

Build a context-rich research agent using Deep Agents and Bedrock AgentCore for multi-step AI workflows

intermediate Published 15 Jun 2026

Action Steps

Build a competitive research agent using Deep Agents
Configure isolated execution environments for the agent
Deploy the agent to Bedrock AgentCore Runtime using the AgentCore CLI
Run the agent as a managed, session-isolated service
Test the agent's performance in a multi-step AI workflow

Who Needs to Know This

Developers building multi-step AI workflows can benefit from this tutorial to create isolated execution environments for their agents, while data scientists and AI engineers can leverage this to improve their research agent's performance

Key Insight

💡 Isolated execution environments can improve the performance and reliability of research agents in multi-step AI workflows

Key Takeaways

Build a context-rich research agent using Deep Agents and Bedrock AgentCore for multi-step AI workflows

Full Article

In this post, you'll build a competitive research agent that demonstrates this pattern end to end. This walkthrough targets developers building multi-step AI workflows who need isolated execution environments for their agents. In Part 2 of the notebook, you can deploy this same agent to Bedrock AgentCore Runtime using the AgentCore CLI, so it runs as a managed, session-isolated service.

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