Building Agents in Java and Spring

In this example-driven session, we'll take a look at how to build both explicit agentic workflows using Spring AI as well as defining autonomous agents using Embabel.

Over the past few years, we've seen the buzz around Generative AI evolve from simple prompts, to document- and tool-augmented prompts, to more formalized collections of tools and prompts in Model Context Protocol (MCP). And for awhile now, agents are all the buzz. Unfortunately, this has presented a paradox wherein everyone knows what agents are and at the same time, nobody knows what agents are.

Regardless of what you think an agent is, it's clear that agents are the most useful when they are able to work within the ecosystems of existing enterprise systems. Since many enterprise systems are based in Java, it would make sense to develop agents in Java so that they can take advantage of prior work that has been developed in Java and the skillsets that were involved. In short, it's unnecessary to develop in Python if you don't already have Python in play in such a system.

Fortunately, frameworks such as Spring AI make easy work of integrating Generative AI in Java. And applying agentic workflow patterns in Java is just as easy. What's more, an agentic framework such as Embabel (which is built on Spring and Spring AI) make developing autonomous, self-planning agents in Java as straightforward as it is to develop web applications or APIs in Java.