Building AI Agents with Spring & MCP

on YouTube ↗40m

" summary

Builds a production-style agentic dog-adoption service: a Java Spring app using Spring AI with Amazon Bedrock (Nova light for chat, a different model for embeddings), Postgres + pgvector as the vector store, rolling chat memory and similarity search over embeddings, and tool-calls moved to a remote MCP scheduler service. The demo creates embeddings, finds “Prancer” by semantic search, issues a scheduled-adopt tool call over MCP, and runs end-to-end successfully.

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" chapters (19)

1. 0:00Welcome
2. 1:55Java for AI
3. 3:18Dog adoption chat client
4. 8:58AWS Bedrock models
5. 11:44Spring Boot setup
6. 14:58Chat model & DB config
7. 18:07AI chat controller
8. 20:10Chat memory
9. 22:37System prompts
10. 24:44RAG embeddings
11. 28:15Vector search
12. 30:01Structured responses
13. 30:23Tool calling
14. 33:14Adoption scheduling
15. 34:17MCP introduction
16. 35:20Spring AI to MCP
17. 37:30MCP integration test
18. 38:55Token monitoring & production
19. 39:57Summary

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