Amazon Bedrock AgentCore enables us to deploy and operate highly capable AI agents securely, at scale. It offers infrastructure purpose-built for dynamic agent workloads, powerful tools to enhance agents, and essential controls for real-world deployment. In this talk, we'll dive deep into various aspects of how to implement an AI Agent in Java using Spring AI, including using the MCP Streamable HTTP Client to talk to the MCP Server with exposed tools. Then, we'll look at the Embabel framework for authoring agentic flows on the JVM that supports intelligent path finding towards goals and cover its benefits. We'll host our agent on Amazon Bedrock AgentCore Runtime and look at other AgentCore features like Observability (in standardized OpenTelemetry compatible format), Gateway (to securely connect to MCP-compatible tools), short and long-term Memory, and Identity. Finally, we'll look at the spring-ai-agentcore project, which provides various integrations between Spring AI and Bedrock AgentCore.

Amazon Bedrock AgentCore enables us to deploy and operate highly capable AI agents securely, at scale. It offers infrastructure purpose-built for dynamic agent workloads, powerful tools to enhance agents, and essential controls for real-world deployment. In this talk, we'll dive deep into various aspects of how to implement an AI Agent in Java using Spring AI, including using the MCP Streamable HTTP Client to talk to the MCP Server with exposed tools. Then, we'll look at the Embabel framework for authoring agentic flows on the JVM that supports intelligent path finding towards goals and cover its benefits. We'll host our agent on Amazon Bedrock AgentCore Runtime and look at other AgentCore features like Observability (in standardized OpenTelemetry compatible format), Gateway (to securely connect to MCP-compatible tools), short and long-term Memory, and Identity. Finally, we'll look at the spring-ai-agentcore project, which provides various integrations between Spring AI and Bedrock AgentCore.