Engineering Scalable AI with Java
summary
The talk explains why the JVM is increasingly used for production-scale, agentic AI systems rather than Python. It covers JVM technologies and features—virtual threads for large-scale agent orchestration, improved JVM memory layout for efficient in-process vector workloads, and type-safe agent frameworks like LangChain4j—to address systems problems such as concurrency, orchestration, memory efficiency, type safety, observability, and supply-chain governance when moving from prototypes to long-running, multi-agent platforms.
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chapters (10)
- 0:00Java and AI introduction
- 3:32Java’s role in AI
- 7:24Java vs Python for AI
- 10:16Java enterprise AI scalability
- 12:22AI agent frameworks in Java
- 15:53Demo: Simple LLM chain
- 18:50Demo: Spring AI integration
- 20:15Demo: MCP protocol with agents
- 23:05Demo: Multi-tool agent system
- 24:50Scaling agents for production
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