Agents, MCP, and Graph Databases: Java Developers Navigate the AI Revolution (#86)

Episode 86 of the Foojay Podcast. All info, show notes, and links are available at https://foojay.io/today/category/podc....

Welcome to another episode of the Foojay Podcast! Today, we're talking about AI and Java, how it's changing the way we work, what we need to watch out for, and why understanding what's really happening matters more than ever.

I recorded these interviews at Devoxx and JFall and spoke with people who are building and using this technology every day. Marianne Hoornenborg opened my eyes to something important: every time an AI generates a token, there's a massive amount of computation happening behind the scenes.

Viktor Gamov and Baruch Sadogursky did something really cool: they tested six different AI coding tools live on stage with the same task. The results were all over the place! But they found that the tools with access to good documentation performed much better.

Stephen Chin showed me how graph databases can make AI responses more reliable by providing a solid source of truth rather than relying on vector search.

Mario Fusco works on LangChain4J, a leading Java framework for AI. He explained that breaking down large tasks into smaller ones and using specialized agents can help reduce errors—hallucinations, as they're called.

Jeroen Benckhuijsen and Martijn Dashorst shared their experiences working with enterprise Java. Even though frameworks are becoming lighter and we're running everything in containers now, there are still complex problems that require real developer expertise.

Maarten Mulders reminds us that AI is a tool, not a replacement—especially when you're solving problems no one has tackled before. You still need to know what you're doing.

And finally, Simon Maple from Tessel discussed moving beyond what he calls "vibe coding" toward a more reliable, production-ready approach, using specifications to guide AI tools.

Content

00:00 Introduction of topics and guests

02:12 Marianne Hoornenborg
  / mhoornenborg  
The Simple Math behind AI
The cost of tokens when using LLMs

06:54 Viktor Gamov and Baruch Sadogursky
  / vikgamov  
  / jbaruch  
Robocoders, about the many agentic tools that can be used for vibe coding
https://context7.com/

16:24 Stephen Chin
  / steveonjava  
Graph versus relational databases
Explaining MCP and Agents

23:09 Mario Fusco
  / mario-fusco-3467213  
AI and LangChain4j in Quarkus
Coding tools with AI

35:43 Jeroen Benckhuijsen
  / jeroenbenckhuijsen  
Java in business, Evolutions in Java
Making use of containers and Kubernetes
Learning from the community

41:44 Martijn Dashorst
  / dashorst  
Investigating an OOM-killer in Kubernetes with the help of AI

49:37 Maarten Mulders
  / mthmulders  
How AI may impact our jobs

56:13 Simon Maple
  / simonmaple  
AI developer tool Tessl
Spec-driven vibe coding
Secure AI development

01:02:12 Conclusion