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