Building production-ready AI Agents with Spring AI, Embabel and Amazon Bedrock AgentCore at AWS Community Day Central Asia 2026

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.

Transcript

1. Building production-ready AI Agents with Spring AI and Amazon Bedrock AgentCore Vadym Kazulkin 10/11/2026 AWS User Groups
2. Contact Vadym Kazulkin ip.labs GmbH Bonn, Germany Co-Organizer of the Java User Group Bonn v.kazulkin@gmail.com https://vkazulkin.com https://www.linkedin.com/in/vadymkazulkin @VKazulkin https://dev.to/vkazulkin https://github.com/Vadym79/ https://de.slideshare.net/VadymKazulkin/ https://www.iplabs.de/
3. Agenda ▪ Introduction to the sample application ▪ Spring AI ▪ Amazon Bedrock AgentCore services ▪ Runtime and Gateway ▪ Observability ▪ Spring AI AgentCore ▪ AgentCore short-term and long-term Memory ▪ Embabel
4. Sample Application
5. Sample Application Conference search application Search criteria: ▪ all conferences ▪ conferences by topic ▪ conferences by date range ▪ conferences by call for papers open date How to talk to this application using natural language? https://github.com/Vadym79/amazon-bedrock-agentcore-spring-ai/tree/main/spring-ai-1.1-conference-search-app-bedrock-agentcore-runtime-mcp-server
6. Spring AI
7. Introduction to Spring AI ▪ Spring AI is an application framework for AI engineering. Its goal is to apply to the AI domain Spring ecosystem design principles such as portability and modular design and promote using POJOs as the building blocks of an application to the AI domain. ▪ Spring AI provides support for all major AI Model providers such as Anthropic, OpenAI, Microsoft, Amazon, Google, and Ollama. The list of its features is rich and new features are constantly added. https://spring.io/projects/spring-ai https://dev.to/aws-heroes/spring-ai-with-amazon-bedrock-part-1-introduction-and-the-sample-application-4hof
8. Spring AI Overview https://docs.spring.io/spring-ai/reference/api/bedrock.html
9. Supported foundation models in Amazon Bedrock https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html
10. Implement MCP Server With Spring AI Expose functionallity like Conference Search Java methods: ▪ all conferences ▪ conferences by topic ▪ conferences by date range ▪ conferences by call for papers open date as MCP tools with Spring AI MCP Server implementation https://github.com/Vadym79/amazon-bedrock-agentcore-spring-ai/tree/main/spring-ai-1.1-conference-search-app-bedrock-agentcore-runtime-mcp-server
11. Implement MCP Server With Spring AI https://github.com/Vadym79/amazon-bedrock-agentcore-spring-ai/tree/main/spring-ai-1.1-conference-search-app-bedrock-agentcore-runtime-mcp-server
12. Implement MCP Server With Spring AI Use spring-ai-starter-mcp-server-webflux dependency for spring.ai.mcp.client.type=ASYNC https://github.com/Vadym79/amazon-bedrock-agentcore-spring-ai/tree/main/spring-ai-1.1-conference-search-app-bedrock-agentcore-runtime-mcp-server
13. Implement MCP Server With Spring AI https://github.com/Vadym79/amazon-bedrock-agentcore-spring-ai/tree/main/spring-ai-1.1-conference-search-app-bedrock-agentcore-runtime-mcp-server
14. Implement MCP Server With Spring AI https://github.com/Vadym79/amazon-bedrock-agentcore-spring-ai/tree/main/spring-ai-1.1-conference-search-app-bedrock-agentcore-runtime-mcp-server
15. Current challenges with development of the AI agents
16. Current challenges with development of the AI agents ▪ Building an AI agent that can handle a real-life use case in production is a complex undertaking. ▪ Although creating a proof of concept demonstrates the potential, moving to production requires addressing scalability, security, observability, and operational concerns that don’t surface in development environments. https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/what-is-bedrock-agentcore.html
17. Introduction to Amazon Bedrock AgentCore
18. Introduction to Amazon Bedrock AgentCore ▪ Amazon Bedrock AgentCore enables you to deploy and operate highly effective agents securely, at scale using any framework and programming language and model (LLM). ▪ With Amazon Bedrock AgentCore, developers can accelerate AI agents into production with the scale, reliability, and security, critical to real-world deployment. ▪ Amazon Bedrock AgentCore services are composable. https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/what-is-bedrock-agentcore.html
19. Amazon Bedrock AgentCore https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/what-is-bedrock-agentcore.html
20. AgentCore Runtime https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/agents-tools-runtime.html
21. Deployment options of Agents prio to AgentCore Runtime
22. AgentCore Runtime Amazon Bedrock AgentCore Runtime provides a secure, serverless and purpose-built hosting environment for deploying and running AI agents or tools. It offers the following benefits: ▪ Framework agnostic - Runtime lets you transform any local agent code to cloud-native deployments with a few lines of code no matter the underlying framework. Works seamlessly with popular frameworks like LangGraph, Strands, and CrewAI. You can also leverage it with custom agents that don't use a specific framework. ▪ Model flexibility - Runtime works with any Large Language Model, such as models offered by Amazon Bedrock, Anthropic Claude, Google Gemini, and OpenAI. https://dev.to/aws-heroes/amazon-bedrock-agentcore-runtime-part-1-introduction-e5i
23. AgentCore Runtime ▪ Protocol support - Service contract supports 2 communication protocols: ▪ HTTP Protocol: Direct REST API endpoints for traditional request/response patterns ▪ MCP Protocol: Model Context Protocol for tools and agent servers ▪ A2A Protocol: Open standard designed to enable seamless communication and collaboration between AI agents. ▪ Extended execution time - Runtime supports both real-time interactions and long-running workloads up to 8 hours, enabling complex agent reasoning and asynchronous workloads that may involve multi-agent collaboration or extended problem-solving sessions. https://dev.to/aws-heroes/amazon-bedrock-agentcore-runtime-part-1-introduction-e5i
24. AgentCore Runtime ▪ Session isolation - In Runtime, each user session runs in a dedicated microVM with isolated CPU, memory, and filesystem resources. This helps create complete separation between user sessions, safeguarding stateful agent reasoning processes and helps prevent cross-session data contamination. After session completion, the entire microVM is terminated and memory is sanitized, delivering deterministic security even when working with non-deterministic AI processes. https://dev.to/aws-heroes/amazon-bedrock-agentcore-runtime-part-1-introduction-e5i
25. AgentCore Runtime Firecracker is the right choice because: • • • Agent sessions vary from milliseconds (single-turn, single-shot, agent interactions with small models), to hours (multi-turn interactions, with thousands of tool calls and LLM interactions). Context varies from zero to gigabytes. The flexibility of Firecracker, including the ability to grow and shrink the CPU and memory use of VMs in place, was a key part of being able to build this economically. https://brooker.co.za/blog/2025/09/18/firecracker.html https://github.com/awslabs/amazon-bedrock-agentcore-samples/tree/main/01-tutorials
26. Hosting MCP Server with Spring AI on AgentCore Runtime ▪ AgentCore supports both stateless and stateful streamable-HTTP MCP servers. By default, stateless mode (stateless_http=True) is recommended for basic MCP servers. The platform automatically adds an Mcp-Session-Id header for any request without one, so MCP clients can maintain connection continuity to the same Amazon Bedrock AgentCore Runtime session. ▪ Spring AI also supports Stateless Streamable-HTTP MCP Servers.
27. Hosting MCP Server with Spring AI on AgentCore Runtime Use spring-ai-starter-mcp-server-webflux dependency for spring.ai.mcp.client.type=ASYNC https://github.com/Vadym79/amazon-bedrock-agentcore-spring-ai/tree/main/spring-ai-1.1-conference-search-app-bedrock-agentcore-runtime-mcp-server
28. Hosting MCP Server with Spring AI on AgentCore Runtime https://github.com/Vadym79/amazon-bedrock-agentcore-spring-ai/tree/main/spring-ai-1.1-conference-search-app-bedrock-agentcore-runtime-mcp-server
29. Hosting MCP Server with Spring AI on AgentCore Runtime https://github.com/Vadym79/amazon-bedrock-agentcore-spring-ai/tree/main/spring-ai-1.1-conference-search-app-bedrock-agentcore-runtime-mcp-server
30. Implement Local MCP Client With Spring AI Use spring-ai-starter-mcp-client-webmvc dependency and spring.ai.mcp.client.type=SYNC for the synchronous communication https://github.com/Vadym79/amazon-bedrock-agentcore-spring-ai/tree/main/spring-ai-1.1-conference-app-agent-local
31. Implement Local MCP Client With Spring AI https://github.com/Vadym79/amazon-bedrock-agentcore-spring-ai/tree/main/spring-ai-1.1-conference-app-agent-local
32. Implement Local MCP Client With Spring AI https://github.com/Vadym79/amazon-bedrock-agentcore-spring-ai/tree/main/spring-ai-1.1-conference-app-agent-local
33. Implement Local MCP Client With Spring AI https://github.com/Vadym79/amazon-bedrock-agentcore-spring-ai/tree/main/spring-ai-1.1-conference-app-agent-local
34. Implement Local MCP Client With Spring AI Spring AI 1.1 Spring AI 2.0 https://github.com/Vadym79/amazon-bedrock-agentcore-spring-ai/tree/main/spring-ai-1.1-conference-app-agent-local https://brightdata.com/blog/ai/sse-vs-streamable-http
35. Implement Local MCP Client With Spring AI https://github.com/Vadym79/amazon-bedrock-agentcore-spring-ai/tree/main/spring-ai-1.1-conference-app-agent-local
36. Implement Local MCP Client With Spring AI For public AgentCore Gateway (no authentication) or the Gateway with IAM authentication, we can autowire the MCP client directly: // For sync client @Autowired private List<McpSyncClient> mcpSyncClients; // OR for async client @Autowired private List<McpAsyncClient> mcpAsyncClients; https://docs.spring.io/spring-ai/reference/api/mcp/mcp-client-boot-starter-docs.html#_streamable_http_transport_properties
37. Implement Local MCP Client With Spring AI Spring AI 1.1 Spring AI 2.0 https://github.com/Vadym79/amazon-bedrock-agentcore-spring-ai/tree/main/spring-ai-1.1-conference-app-agent-local
38. Implement Local MCP Client With Spring AI Spring AI 1.1 https://blog.modelcontextprotocol.io/posts/2026-07-28/ https://aws.amazon.com/de/blogs/machine-learning/how-agentcore-gateway-supports-the-mcp-2026-07-28-spec/
39. Spring AI Tool “Search Tool” https://github.com/spring-ai-community/spring-ai-tool-search-tool https://spring.io/blog/2025/12/11/spring-ai-tool-search-tools-tzolov
40. Implement Local MCP Client With Spring AI http GET http://localhost:8080/conference?prompt="Please provide me with the list of conferences, including their IDs, with Java topics happening in 2026 and 2027, with the call for papers open today" Content-Type:text/plain. https://github.com/Vadym79/amazon-bedrock-agentcore-spring-ai/tree/main/spring-ai-1.1-conference-app-agent-local
41. Implement MCP Server With Spring AI Expose functionallity like Conference Search Java methods: ▪ all conferences ▪ conferences by topic ▪ conferences by date range ▪ conferences by call for papers open date as MCP tools with Spring AI MCP Server implementation https://github.com/Vadym79/amazon-bedrock-agentcore-spring-ai/tree/main/spring-ai-1.1-conference-search-app-bedrock-agentcore-runtime-mcp-server
42. Sample Application Expose additional functionallity like : ▪ create talk ▪ search for talks with title substring ▪ apply for conferences with the talks https://github.com/Vadym79/amazon-bedrock-agentcore-spring-ai/tree/main/conference-talks-and-applications-app
43. AgentCore Gateway https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/gateway.html
44. AgentCore Gateway Amazon Bedrock AgentCore Gateway is a fully-managed AI gateway that provides a single, secure entry point for agentic traffic— connecting agents to tools, to other agents, and to large language models (LLMs). https://dev.to/aws-heroes/amazon-bedrock-agentcore-runtime-part-1-introduction-e5i
45. AgentCore Gateway ▪ AgentCore Gateway acts like an MCP server, providing a single access point for an agent to interact with its tools. A Gateway can have multiple targets, each representing a different tool or set of tools. ▪ AgentCore Gateway supports OpenAPI, Smithy, Amazon API Gateway and AWS Lambda, MCP Servers and Integrations as Gateway target types . https://dev.to/aws-heroes/amazon-bedrock-agentcore-runtime-part-1-introduction-e5i
46. AgentCore Gateway MCP Target
47. AgentCore Gateway https://github.com/Vadym79/amazon-bedrock-agentcore-spring-ai/tree/main/spring-ai-1.1-conference-app-bedrock-agentcore-cdk
48. AgentCore Gateway https://github.com/Vadym79/amazon-bedrock-agentcore-spring-ai/tree/main/spring-ai-1.1-conference-app-bedrock-agentcore-cdk
49. AgentCore Gateway https://github.com/Vadym79/amazon-bedrock-agentcore-spring-ai/tree/main/spring-ai-1.1-conference-app-bedrock-agentcore-cdk
50. Implement Local MCP Client With Spring AI https://github.com/Vadym79/amazon-bedrock-agentcore-spring-ai/tree/main/spring-ai-1.1-conference-app-agent-local
51. Implement Local MCP Client With Spring AI http GET http://localhost:8080/conference?prompt=“Please create a talk with some cool title (max 60 charachters long) and description (max 300 charachters long) about using Spring AI on Amazon Bedrock AgentCore service. Then provide me with the list of conferences including their ids with Java topic hapenning in 2027 with call for papers open today. Finally, for each conference, apply individually for the it with the talk just created.“ Content-Type:text/plain. https://github.com/Vadym79/amazon-bedrock-agentcore-spring-ai/tree/main/spring-ai-1.1-conference-app-agent-local
52. Implement Local MCP Client With Spring AI https://github.com/Vadym79/amazon-bedrock-agentcore-spring-ai/tree/main/spring-ai-1.1-conference-app-agent-local
53. Deploy MCP Client With Spring AI on AgentCore Runtime Using Custom Agent to deploy it on AgentCore Runtime gives us full control over our agent's HTTP interface. To deploy a custom agent using Docker, follow AgentCore Runtime requirements : ▪ /invocations Endpoint: POST endpoint for agent interactions ▪ /ping Endpoint: GET endpoint for health checks https://dev.to/aws-heroes/amazon-bedrock-agentcore-runtime-part-4-using-custom-agent-with-strands-agents-sdk-201o
54. Deploy MCP Client With Spring AI on AgentCore Runtime https://github.com/Vadym79/amazon-bedrock-agentcore-spring-ai/tree/main/spring-ai-1.1-conference-app-agent-bedrock-agentcore-runtime/agent
55. Deploy MCP Client With Spring AI on AgentCore Runtime https://github.com/Vadym79/amazon-bedrock-agentcore-spring-ai/tree/main/spring-ai-1.1-conference-app-agent-bedrock-agentcore-runtime/agent
56. Deploy MCP Client With Spring AI on AgentCore Runtime https://github.com/Vadym79/amazon-bedrock-agentcore-spring-ai/tree/main/spring-ai-1.1-conference-app-agent-bedrock-agentcore-runtime/agent
57. AgentCore Memory https://github.com/spring-ai-community/spring-ai-agentcore
58. Spring AI Memory ▪ InMemoryChatMemoryRepository ▪ Default ChatMemory provider to store messages in memory using a ConcurrentHashMap (by default the last 20, but you can set a different value). Not very suitable for hosting on AgentCore Runtime. ▪ JdbcChatMemoryRepository ▪ CassandraChatMemoryRepository ▪ Neo4j ChatMemoryRepository ▪ CosmosDBChatMemoryRepository ▪ MongoChatMemoryRepository https://docs.spring.io/spring-ai/reference/api/chat-memory.html#_in_memory_repository
59. Spring AI Memory Use defaultAdvisors of the ChatClient.Builder to configure the chat memory, as by default all conversations are stateless and you can't ask question about previously delivered content. https://docs.spring.io/spring-ai/reference/api/chat-memory.html#_in_memory_repository
60. AgentCore Memory AgentCore Memory lets your AI agents deliver intelligent, contextaware, and personalized interactions by maintaining both immediate and long-term knowledge. AgentCore Memory offers two types of memory: . ▪ Short-term memory: Stores conversations to keep track of immediate context. ▪ Long-term memory : Stores extracted insights - such as user preferences, semantic facts, and summaries - for knowledge retention across sessions. https://dev.to/aws-heroes/amazon-bedrock-agentcore-runtime-part-6-using-agentcore-short-term-memory-with-strands-agents-sdk-55d4
61. Spring AI AgentCore Short-Term Memory https://github.com/Vadym79/amazon-bedrock-agentcore-spring-ai/tree/main/spring-ai-2.0-ac-conference-app-agent-bedrock-agentcore-runtime/agent https://vkazulkin.com/building-ai-agents-with-spring-ai-and-amazon-bedrock-agentcore-part-7-use-spring-ai-agentcore-short-term-memory-for-mcp-client-on-agentcore-runtime/
62. Spring AI AgentCore Long-Term Memory https://github.com/Vadym79/amazon-bedrock-agentcore-spring-ai/tree/main/spring-ai-2.0-ac-conference-app-agent-bedrock-agentcore-runtime/agent https://vkazulkin.com/building-ai-agents-with-spring-ai-and-amazon-bedrock-agentcore-part-8-use-spring-ai-agentcore-long-term-memory-for-mcp-client-on-agentcore-runtime/
63. Spring AI AgentCore Memory https://github.com/Vadym79/amazon-bedrock-agentcore-spring-ai/tree/main/spring-ai-2.0-ac-conference-app-agent-bedrock-agentcore-runtime/agent https://vkazulkin.com/building-ai-agents-with-spring-ai-and-amazon-bedrock-agentcore-part-8-use-spring-ai-agentcore-long-term-memory-for-mcp-client-on-agentcore-runtime/
64. Spring AI AgentCore Memory prompt=“You recently applied for some conferences for me. Can you provide me with the details?“ https://github.com/Vadym79/amazon-bedrock-agentcore-spring-ai/tree/main/spring-ai-2.0-ac-conference-app-agent-bedrock-agentcore-runtime/agent https://vkazulkin.com/building-ai-agents-with-spring-ai-and-amazon-bedrock-agentcore-part-8-use-spring-ai-agentcore-long-term-memory-for-mcp-client-on-agentcore-runtime/
65. AgentCore Observability https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/observability.html
66. AgentCore Observability ▪ AgentCore Observability gives us real-time visibility into agent operational performance through access to dashboards powered by Amazon CloudWatch and telemetry for key metrics such as session count, latency, duration, token usage, and error rates. ▪ AgentCore emits telemetry data in standardized OpenTelemetry (OTEL)-compatible format, enabling us to easily integrate it with our existing monitoring and observability stack. https://dev.to/aws-heroes/amazon-bedrock-agentcore-runtime-part-1-introduction-e5i
67. AgentCore Observability with ADOT ▪ To view this data in the CloudWatch we need to add the AWS Distro for Open Telemetry (ADOT) SDK to our agent code. ▪ ADOT is a secure, production-ready, AWS-supported distribution of the OpenTelemetry project. Part of the Cloud Native Computing Foundation, OpenTelemetry provides open source APIs, libraries, and agents to collect distributed traces and metrics for application monitoring. ▪ With ADOT, we can instrument our applications just once to send correlated metrics and traces to multiple AWS and Partner monitoring solutions. In our case we will send the metrics to the CloudWatch GenAI Observability service. https://dev.to/aws-heroes/amazon-bedrock-agentcore-runtime-part-1-introduction-e5i
68. AgentCore Observability https://github.com/Vadym79/amazon-bedrock-agentcore-spring-ai/tree/main/spring-ai-1.1-conference-app-bedrock-agentcore-cdk
69. AgentCore Observability https://github.com/Vadym79/amazon-bedrock-agentcore-spring-ai/tree/main/spring-ai-1.1-conference-app-bedrock-agentcore-cdk
70. AgentCore Observability https://github.com/Vadym79/amazon-bedrock-agentcore-spring-ai/tree/main/spring-ai-1.1-conference-app-bedrock-agentcore-cdk
71. AgentCore Observability https://github.com/Vadym79/amazon-bedrock-agentcore-spring-ai/tree/main/spring-ai-1.1-conference-app-bedrock-agentcore-cdk https://vkazulkin.com/building-ai-agents-with-spring-ai-and-amazon-bedrock-agentcore-part-9-configure-agentcore-observability/
72. AgentCore Observability https://github.com/Vadym79/amazon-bedrock-agentcore-spring-ai/tree/main/spring-ai-1.1-conference-app-bedrock-agentcore-cdk
73. AgentCore Observability https://github.com/Vadym79/amazon-bedrock-agentcore-spring-ai/tree/main/spring-ai-1.1-conference-app-bedrock-agentcore-cdk
74. AgentCore Observability https://github.com/Vadym79/amazon-bedrock-agentcore-spring-ai/tree/main/spring-ai-1.1-conference-app-bedrock-agentcore-cdk
75. AgentCore Observability https://github.com/Vadym79/amazon-bedrock-agentcore-spring-ai/tree/main/spring-ai-1.1-conference-app-bedrock-agentcore-cdk
76. AgentCore Observability https://github.com/Vadym79/amazon-bedrock-agentcore-spring-ai/tree/main/spring-ai-1.1-conference-app-bedrock-agentcore-cdk
77. AgentCore Observability https://github.com/Vadym79/amazon-bedrock-agentcore-spring-ai/tree/main/spring-ai-1.1-conference-app-bedrock-agentcore-cdk
78. Embabel Framework https://github.com/embabel/embabel-agent
79. Embabel 1. Please create a talk with some cool title (max 60 charachters long) and description (max 300 charachters long) about using Spring AI on Amazon Bedrock AgentCore service. 2. Then provide me with the list of conferences including their ids with Java topic hapenning in 2027 with call for papers open today. 3. Finally, for each conference, apply individually for the it with the talk just created.
80. Embabel Embabel is a framework for authoring agentic flows on the JVM that seamlessly mix LLM-prompted interactions with code and domain models. Supports intelligent path finding towards goals. https://github.com/embabel/embabel-agent
81. Embabel https://github.com/embabel/embabel-agent
82. Embabel Agents, Actions and Goals https://github.com/Vadym79/amazon-bedrock-agentcore-spring-ai/tree/main/embabel-1.x-conference-app-agent-local https://github.com/Vadym79/amazon-bedrock-agentcore-spring-ai/tree/main/embabel-1.5.x-conference-app-agent-local
83. Embabel Agents, Actions and Goals https://github.com/Vadym79/amazon-bedrock-agentcore-spring-ai/tree/main/embabel-1.x-conference-app-agent-local https://github.com/Vadym79/amazon-bedrock-agentcore-spring-ai/tree/main/embabel-1.5.x-conference-app-agent-local
84. Embabel Agents, Actions and Goals https://github.com/Vadym79/amazon-bedrock-agentcore-spring-ai/tree/main/embabel-1.x-conference-app-agent-local https://github.com/Vadym79/amazon-bedrock-agentcore-spring-ai/tree/main/embabel-1.5.x-conference-app-agent-local
85. Embabel Agents, Actions and Goals Please create a talk with some cool title (max 60 charachters long) and description (max 300 charachters long) about using Spring AI on Amazon Bedrock AgentCore service. Then provide me with the list of conferences including their ids with Java topic hapenning in 2027 with call for papers open today. Finally, for each conference, apply individually for the it with the talk just created.
86. Embabel Agents, Actions and Goals Please create a talk with some cool title (max 60 charachters long) and description (max 300 charachters long) about using Spring AI on Amazon Bedrock AgentCore service. Then provide me with the list of conferences including their ids with Java topic hapenning in 2027 with call for papers open today. Finally, for each conference, apply individually for the it with the talk just created. https://github.com/Vadym79/amazon-bedrock-agentcore-spring-ai/tree/main/embabel-1.x-conference-app-agent-local https://github.com/Vadym79/amazon-bedrock-agentcore-spring-ai/tree/main/embabel-1.5.x-conference-app-agent-local
87. Embabel Agents, Actions and Goals Please create a talk with some cool title (max 60 charachters long) and description (max 300 charachters long) about using Spring AI on Amazon Bedrock AgentCore service. Then provide me with the list of conferences including their ids with Java topic hapenning in 2027 with call for papers open today. Finally, for each conference, apply individually for the it with the talk just created.
88. Wrap Up
89. Amazon Bedrock AgentCore https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/what-is-bedrock-agentcore.html
90. Amazon Bedrock AgentCore Amazon Bedrock AgentCore services are composable. https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/what-is-bedrock-agentcore.html
91. Amazon Bedrock AgentCore • We used Spring AI with Amazon, Bedrock and API Gateway and Lambda as MCP-Servers via AgentCore Gateway. • We can use any models (Open AI) supported by Spring AI and any MCP Servers with exposed tools similarly. https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/what-is-bedrock-agentcore.html
92. Amazon Bedrock AgentCore • AgentCore Runtime is framework and programming language agnostic, so we can use Java frameworks like Embabel, LangChain4j, Koog, and also Python frameworks like LangChain, LangGraph, Strands Agents, Pydantic AI, CrewAI and others to host the agents there. https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/what-is-bedrock-agentcore.html
93. Article Series: “Building AI Agents with Spring AI and Amazon Bedrock AgentCore” Article series covers the topics from this talk in more depth https://vkazulkin.com/series/building-ai-agents-with-spring-ai-and-amazon-bedrock-agentcore/
94. “Building AI Agents with Spring AI and Amazon Bedrock AgentCore” on GitHub https://github.com/Vadym79/amazon-bedrock-agentcore-spring-ai
95. Ask Me Anything
96. Thank you