Building AI Agents Using Java + Spring Boot

Ask a room of engineers where you’d build an AI agent and the answer comes back almost as a reflex: Python. The tutorials are in Python, the SDKs ship Python-first, and every demo on your feed imports the same three libraries. So the enterprises running tens of millions of lines of Java on the JVM quietly assume agents aren’t for them yet.

They’re wrong, and the reason is almost boring. An agent is mostly plumbing — a loop that calls a model, runs a tool, feeds the result back, and repeats under supervision. Spring Boot has spent fifteen years being very good at exactly that kind of plumbing: dependency injection, configuration, retries, observability, and a request lifecycle you can reason about. With Spring AI, the model call is just one more bean. If your system of record already lives in Java, you don’t need to stand up a second service in another language just to add an agent to it.

Why Java for agents at all?

The honest answer is: because that’s where the data and the rules already are. Banks, insurers, logistics, retail backends — the unglamorous systems that actually run the economy — are overwhelmingly JVM shops. Putting the agent next to the domain logic it needs to act on beats marshalling everything across a network boundary to a Python sidecar. Spring AI gives you a provider-agnostic ChatClient, tool calling, embeddings, and vector-store integrations using the same idioms Spring developers already know, so the agent inherits your existing security, transactions, and metrics for free.

• A model client that abstracts the provider, so swapping a hosted model for a local or Azure-hosted one is a config change, not a rewrite.
• Tool calling — a way to expose functions the model can invoke, with typed inputs and outputs you control.
• Memory and context management, so a conversation or task carries state without you stuffing everything into one giant prompt.
• Observability and guardrails: timeouts, retries, token accounting, and logs, because an agent that calls tools in a loop is a production service, not a script.

The shape of a Spring Boot agent

Strip the hype away and an agent is a controlled loop. The model proposes an action, your code decides whether to run it, the result goes back into the context, and the loop continues until the model produces a final answer or you cut it off. In Spring AI that loop is mostly handled for you — you describe the tools and let the ChatClient orchestrate the calls.

@RestController
class AssistantController {

  private final ChatClient chat;

  AssistantController(ChatClient.Builder builder, OrderTools orderTools) {
    // Register tools once; the model decides when to call them.
    this.chat = builder
        .defaultSystem("You are a support agent. Use tools for any order data.")
        .defaultTools(orderTools)
        .build();
  }

  @PostMapping("/ask")
  String ask(@RequestBody String question) {
    return chat.prompt()
        .user(question)
        .call()          // Spring AI runs the tool-call loop under the hood
        .content();
  }
}

That’s the whole entry point. The model receives the question, and if answering it needs live data, it asks to call a tool. Spring AI executes the call, hands the result back to the model, and only returns to your controller when there’s a final answer. You didn’t write the loop — but you do own every tool it can reach, which is exactly where your attention belongs.

Tools are just beans

This is the part that makes Java feel native to agents instead of bolted on. A tool is a plain method with a description and typed parameters. Spring generates the schema the model needs from your types, and dependency injection means the tool already has its repository, its database connection, and its security context — no globals, no passing clients around by hand.

@Component
class OrderTools {

  private final OrderRepository orders;

  OrderTools(OrderRepository orders) {
    this.orders = orders;
  }

  @Tool(description = "Look up the current status of an order by its ID")
  OrderStatus orderStatus(String orderId) {
    return orders.findById(orderId)
        .map(o -> new OrderStatus(o.id(), o.state(), o.eta()))
        .orElseThrow(() -> new IllegalArgumentException("No such order"));
  }
}

Where teams get it wrong

• Blocking the request thread. A tool that calls a slow API on the main thread will exhaust your pool the moment traffic arrives. Use timeouts, and push anything slow off the request path.
• No ceiling on the loop. Always cap how many tool calls one request can trigger — an agent that loops on a bad tool result will happily burn your token budget in seconds.
• Unbounded memory. Dumping an entire chat history into every prompt is how you turn a cheap call into an expensive one. Summarize or window the context.
• Trusting tool output as if the model wrote it. The model decides what to call; your code decides what’s allowed. Validate arguments and authorize every action server-side.
• Logging nothing. If you can’t see which tools fired with which arguments, you can’t debug the agent or prove what it did. Treat traces as a feature, not an afterthought.

The interesting part of an agent isn’t the model call — it’s the boring scaffolding around it. Java has been quietly excellent at boring scaffolding for two decades, which is exactly why it deserves a seat at this table.

When Java earns its place — and when it doesn’t

I won’t pretend the ecosystem is even. If you’re doing exploratory work, fine-tuning, or anything that lives close to the research frontier, Python’s libraries are years ahead and you should just use them. The Java case is different: it’s for shipping an agent into a system that already exists, where the cost of a parallel Python service — its own deploys, its own auth, its own on-call — outweighs the convenience of nicer notebooks.

1. Your agent needs to act on data and rules that already live in a JVM service — keep it close and build it in Spring.
2. You need enterprise-grade security, transactions, and observability around every tool call — Spring gives you those by default.
3. You’re prototyping, fine-tuning, or chasing the latest research technique — reach for Python and don’t look back.
4. Let the rest of your stack decide the language, not the demo you happened to watch this week.

The lesson isn’t that Java is the best language for AI agents; it’s that “the best language” is the wrong frame. The best place to build an agent is next to the code it has to work with — and for a very large slice of the world’s software, that place runs on the JVM. Spring Boot just lets you admit it.