Part 1 of 2 in the “AI Agent Memory” series. Short-Term Memory with SpringBoot + LangChain4j. The Demo that wasn’t We got back from our offsite pretty excited. We had just walked through a POC showing how metadata actually earns its keep in an AI-first world. With a solid semantic layer, our analysts could spend more time cooking and less time wandering around the data pantry asking, “where did we put the salt again?” The demo was slick. Agents could find the right data, make suggestions, even tap into technical metadata to build and run queries for users. It felt…
J O H N R A . M E Posts
It’s a Friday afternoon, and I catch part of a conversation between a couple of colleagues. They’re trying to make sense of what “skills” means for a multi-agent platform we’re planning. One of the engineers (relatively new, but not inexperienced) asked a fair question: “Are we just expected to write a bunch of markdown files?” Engineers coming from Copilot or Claude often get tripped up by the word “skill.” The file, SKILL.md, tends to be the frame of reference. It mean something specific and narrow. The more senior engineer is trying to explain, but it’s not quite landing. The terminology…
A 3-Part Series: Agents, Workflows, and Skills – Build the Right Thing In Part 1, we built a bug investigation agent. In Part 2, we built a content quality pipeline. Both worked. Both had AI doing something I hope you find genuinely useful. But if you read them back to back with a sufficiently critical eye – the kind of eye a good code reviewer develops after seeing the same mistake for the fifth time – you’d notice something I deliberately left in both systems: capability reuse. The bug agent could search code. The content pipeline had its own policy checker.…
A 3-Part Series: Agents, Workflows, and Skills – Build the Right Thing There’s a phrase I’ve used in engineering reviews for years, usually right before someone’s six-week project gets redirected: “Don’t hire a strategist when you need a soldier.” In Part 1, we discuss agents – an autonomous AI system that reasons its way through open-ended problem. If you read it, you know I’m a fan. They’re genuinely capable, and when you deploy one in the right context, it feels like a superpower. But here’s the thing nobody says out loud at AI conferences: most of what you actually need to build…
A 3-Part Series: Agents, Workflows, and Skills – Build the Right Thing Every few years, “intelligent automation” gets a fresh coat of paint – and a fresh wave of hype – while leaving behind a familiar trail of abandoned projects. Expert systems. Neural nets. Rule engines. Ontologies (remember those? Well… it’s coming back. That’ll be a separate blog). Even microservices got swept into the narrative at one point. And now: agents. Here’s the take: this wave is meaningfully different. But the failure mode hasn’t changed. Engineers still tend to grab the shiny new hammer before fully understanding the nail –…