A better approach is to delegate deep work to isolated subagents that return only concise results. LangChain Deep Agents handles this orchestration, spawning specialized ephemeral subagents and managing their lifecycle. Amazon Bedrock AgentCore provides the infrastructure each subagent needs. This includes a real browser in a MicroVM (a lightweight, single-purpose virtual machine) for web research and a full Python environment for data analysis. AgentCore is also available as a native sandbox provider in the Deep Agents CLI, so you can run deepagents --sandbox agentcore to try AgentCore CodeInterpreter without building a full agent.

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.

Your coordinator agent receives the request and first checks AgentCore Memory for past research insights. It then spawns three browser subagents in parallel for research, each navigating a different competitor’s website in its own AgentCore Browser MicroVM. When the three return structured findings, an analyst subagent receives the combined data and uses an AgentCore Code Interpreter to generate a comparison chart and markdown report. Finally, key insights are saved to AgentCore Memory for future sessions. You can trace the entire workflow with Amazon CloudWatch through Amazon Bedrock AgentCore Observability or LangSmith.

Each subagent type accesses only its specific set of tools: browser tools for the researchers, interpreter tools for the analyst, and memory tools for the coordinator.

Figure 1: Solution architecture showing the data flow between the LangChain Deep Agents orchestrator, Amazon Bedrock AgentCore Browser MicroVMs, Interpreter, Memory, and CloudWatch or LangSmith tracing.