What happened
A new AWS Machine Learning post argues that an agent operating in a notebook is not the same as an agent running in production, setting the stage for a migration walkthrough.
The walkthrough covers moving a LangGraph customer support agent to Amazon Bedrock AgentCore in two phases: first adopting Runtime, Gateway, and Memory, and then shifting to model-driven planning with Strands Agents.
Why it matters
Many agent prototypes work in isolation but fail to meet production requirements around reliability, scalability, and manageability. Using Bedrock AgentCore provides a structured path for maturing these workloads.
The staged approach lets teams gradually migrate components while retiring operational burdens, rather than performing a risky, all-at-once rewrite.
Key facts
The migration applies to a LangGraph customer support agent.
Stage one of the migration moves the agent onto Bedrock AgentCore's Runtime, Gateway, and Memory.
Stage two adopts model-driven planning on Strands Agents.
The migration retires operational burdens along the way.
What to watch next
Teams running notebook-based agents should watch for patterns that address production gaps like memory, routing, and governance.
The two-stage migration approach may indicate how Bedrock AgentCore features such as Strands Agents evolve for broader agentic use cases.