Build Context-Rich Research Agents with Deep Agents and Bedrock
▶ The 2-minute explainer
Summary
This post guides developers in building competitive research agents using Deep Agents and Bedrock AgentCore, focusing on multi-step AI workflows and isolated execution environments. It demonstrates deploying these agents as managed, session-isolated services via the AgentCore CLI.
Why it matters
Professionals can learn to build sophisticated AI agents capable of complex research tasks, leveraging managed services for scalable and isolated execution, which is critical for developing reliable and secure AI applications.
How to implement this in your domain
- 1Familiarize yourself with Deep Agents and Amazon Bedrock AgentCore functionalities.
- 2Follow the provided walkthrough to construct a multi-step competitive research agent.
- 3Design your AI agents to operate within isolated execution environments for enhanced security and stability.
- 4Utilize the AgentCore CLI to deploy and manage your research agents as session-isolated services.
- 5Adapt the demonstrated patterns to build other context-rich AI agents for various business needs.
Who benefits
Key takeaways
- Deep Agents and Bedrock AgentCore enable building context-rich research agents.
- The approach supports multi-step AI workflows with isolated execution.
- Agents can be deployed as managed, session-isolated services.
- This enhances the reliability and scalability of AI-powered research.
Original post by Sundar Raghavan
"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 a…"
View on XOriginally posted by Sundar Raghavan on X · view source
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