Introduction

“Agent” has become the word for any program that calls an LLM more than once, which makes it a word worth being precise about. An agent, in the sense this post uses, is a loop: the model decides which tool to call next, your code executes it, and the result feeds back in — repeating until the model decides it’s done. That’s a genuinely different (and riskier) shape than a single request/response call.

Introduction

“Agent” has become the word for any program that calls an LLM more than once, which makes it a word worth being precise about. An agent, in the sense this post uses, is a loop: the model decides which tool to call next, your code executes it, and the result feeds back in — repeating until the model decides it’s done. That’s a genuinely different (and riskier) shape than a single request/response call.

Introduction

LLMs are usually associated with Python, but a great deal of production software — banking, enterprise backends, long-lived services — runs on the JVM, and those systems increasingly need to call language models too. Java’s strong typing and mature tooling are genuine assets here: they push you toward exactly the discipline reliable LLM applications require.

The core mindset is the same in any language: treat model output as a hypothesis to verify, not a fact to trust. This post covers the practices that make Java LLM applications production-grade, using Anthropic’s Claude and the official anthropic-java SDK.

Introduction

Calling an LLM API is easy. Building an application on top of one that is reliable — that fails predictably, doesn’t hallucinate its way into wrong answers, and doesn’t surprise you with a bill — is a real engineering discipline.

The core mindset shift: treat model output as a hypothesis to verify, not a fact to trust. This post covers the practices that make Python LLM applications production-grade, using Anthropic’s Claude and the official anthropic SDK.