~/talk

GenAI Grows Up: Building Production-Ready Agents on the JVM

github:johnsonr
slides:none on file

Argues that enterprise GenAI needs JVM-first, strongly typed agents and shows Embabel, an open-source JVM framework that integrates with Spring integration, uses Kotlin/Java records, and implements deterministic planning via a goal‑oriented action planner (not an LLM) to chain typed actions and LLM calls. Claims orchestration and structure remove 80–90% of prompt fragility. Demonstrates working agents: a slide‑deck generator (90% auto‑written using GPT‑4.1, GPT‑4.1‑mini, Claude 37 Sonnet), a travel planner, and a bank support agent with unit tests and predictable failure handling.

AI-written summary · about

  1. 0:00Intro
  2. 1:53Let's make this personal
  3. 2:24Tools add power
  4. 3:36"Personal assistant" use case
  5. 4:30Powerful but not predictable is sometimes OK
  6. 6:45Alarming failure rate
  7. 7:19Why do GenAI projects fail?
  8. 9:56Prompt engineering: The new alchemy
  9. 12:48Integration problems
  10. 13:28Excessive vendor influence
  11. 13:57Top-down mandates
  12. 15:15Organizational issues
  13. 15:55How do we fix this?
  14. 21:112. Integrate with what works
  15. 22:30Bringing structure to LLM interactions
  16. 23:42Domain-integrated context engineering
  17. 26:47What is the role of Java developers?
  18. 27:27Option 1: Imitate Python approaches
  19. 28:20Option 2: Build something better
  20. 29:10The stakes are high
  21. 29:46What the JVM brings to GenAI
  22. 30:32Python vs JVM for enterprise AI
  23. 32:22Time for Java community to lead again
  24. 33:01Introducing Embabel
  25. 39:19Java example: Bank support agent
  26. 41:40Seamless code actions & LLM invocation
  27. 42:29Builds on Spring
  28. 44:03Modern Java/Kotlin API
  29. 46:01Example: This slide deck
  30. 49:24Decker agent example
  31. 50:04Embabel vs Python frameworks
  32. 51:22Conclusion
  33. 52:38Start right away
  34. 53:32Outro
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