Migrating Agentic Workloads to Amazon Bedrock AgentCore

Migrating agentic workloads from development environments to production involves addressing operational burdens such as session management, tool authentication, and state durability. Amazon Bedrock AgentCore offers a platform to build, connect, and optimize agents at scale, with managed compute, tools, and durable state.

The migration process is outlined in two main stages. Stage 1 involves moving the agent onto Amazon Bedrock AgentCore Runtime, Gateway, and Memory, without altering the agent's reasoning or behavior. This transition offloads operational burdens like OS patching, session isolation, and tool authentication to managed services.

Stage 2 involves rebuilding the agent's planning loop as model-driven planning on Strands Agents, further abstracting the operational code from the agent logic. This stage enables switching models via configuration rather than redeployment, simplifying updates.

The article provides a detailed walkthrough, including prerequisites such as AWS account setup, Python environment, and resource creation. It demonstrates how to modify existing LangGraph agents, migrate tool calls to Gateway, and implement durable conversation state with AgentCore Memory.

The final stage, Stage 3, involves handing over the agent loop to an AgentCore harness powered by Strands Agents, fully removing the need for custom code to manage the inference loop. This transition allows for model changes through configuration, not code changes.

Operational pitfalls such as assuming feature parity, holding state in-memory, and authentication gaps are discussed, emphasizing the importance of defining clear acceptance criteria and mapping authentication flows.

Cleanup procedures are also provided to remove resources and avoid ongoing charges. Overall, the migration enables scalable, managed, and durable agent deployment on AWS, reducing operational overhead and increasing flexibility.