AWS publishes Bedrock AgentCore migration path for production agentic workloads

The migration guide targets the gap between agent prototypes and production: it shows moving a LangGraph customer-support agent to Bedrock AgentCore in two stages — first onto Runtime, Gateway and Memory, then to model-driven planning on Strands Agents. The goal is getting agents out of notebooks and into governed, production-grade deployments with observability, identity and lifecycle management.

Technically, AgentCore now supports server-side tool use — web searches, database updates and similar actions executed inside AWS security boundaries rather than round-tripping through client code — plus extended prompt caching that makes long, multi-turn agent conversations more cost-effective. The addition of Web Search on Bedrock in AWS GovCloud (US-West) extends grounded, citation-backed responses to compliance-sensitive government and public-sector workloads, a segment where data-residency and auditability requirements block many commercial AI tools.

Strategically this is AWS competing on the agent-platform layer against Anthropic's shopping blueprint, Google's Managed Agents and Microsoft's Foundry/Agent 365 stack. The differentiator AWS is pressing is enterprise governance: keeping tool execution, memory and identity within its security perimeter, backed by the AWS Agent Registry that VP Swami Sivasubramanian recently made generally available for cataloging and governing every agent, tool and skill across an organization. For enterprises, the value proposition is running agents that can autonomously act — search, update databases, call APIs — without leaving AWS's compliance envelope. The practical test is whether the LangGraph-to-AgentCore migration is smooth enough that teams already invested in open frameworks make the jump.