Migrate agentic workloads to Amazon Bedrock AgentCore
An agent that works in a notebook is not an agent in production. This post walks through migrating a LangGraph customer support agent to Amazon Bedrock AgentCore in two stages: onto Runtime, Gateway, and Memory, then to model-driven planning on Strands Agents, retiring operational burdens along the way.
Migrating production agentic workloads to Amazon Bedrock AgentCore involves shedding operational burdens and transitioning the agent to a managed service. The migration consists of two stages: Stage 1 moves the agent to Amazon Bedrock AgentCore Runtime, Gateway and Memory, while Stage 2 introduces model-driven planning using Strands Agents.
The first stage transitions the agent onto the Bedrock AgentCore Runtime, Gateway and Memory, without altering the agent's reasoning logic. This results in a hosted agent with managed tools and durable state. The second stage, where the agent starts planning model-driven planning, can be skipped if desired, as stage 1 already provides a managed solution.
The migration process involves four key constructs that touch the agent's code: build_graph(), Agent(model=..., system_prompt=..., tools=...), Runtime : BedrockAgentCoreApp, and MemorySaver() with thread_id in the invoke config. Other aspects, such as tool authentication, checkpoint storage, and IAM policies, remain under the agent owner's control throughout the migration.
The migration allows for scaling, patching, and isolation of the agent's compute resources. Observability is handled by default, with Runtime logs, metrics and traces sent to Amazon CloudWatch. This migration approach aims to simplify operational responsibilities while maintaining the agent's reasoning capabilities.
Written by urgent.news from AWS Machine Learning's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
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