Ayush is a backend engineer who builds scalable, secure, production-ready systems with Java and the Spring ecosystem. He works on microservices, REST APIs, event-driven services and cloud-native applications, and recently focuses on AI-powered backend engineering with Spring AI: LLMs, RAG, vector databases and agents. He founded AgentWorks HQ to share what he learns through technical writing.
View all authorsIn Part 2 you learned what tokens, the context window, temperature and embeddings are. Now you use a model from real Java code.
By the end you will have a Spring Boot app with one endpoint, /ask. You send it a question. It passes the question to a model and returns the answer. You will also log how many tokens each call used, and make the app fail politely when the model is down.
Plan for 30 to 40 minutes. You need Java 17 or newer, Maven, an editor, and either Ollama (a free tool that runs models on your own machine) or an account with a hosted model provider.
You write Java for a living, and you have heard that Claude Code can work on your project from the terminal. This is a safe, small way to try it on real code.
You will install it, start it on a Spring Boot project, fix a failing test and add a new endpoint. You will see every prompt, every question it asks and every change it makes.
You write Java for a living. You have probably used a chatbot, and you may have wondered what happens inside it. This article answers that in terms you already know.
By the end, you will understand four ideas: tokens, the context window, sampling and embeddings. Everything else in this series builds on them. Each one gets a plain explanation, a Java comparison, and a warning about the mistake people make first.
You do not need any maths. You do not need Python. You will read one small Java program and look at three simple diagrams. If you have not read the map yet, start with Part 1, the roadmap. This article is the first step of Stage 1.
You already write Java for a living. You can build AI features without leaving it. Here is the plan.
This series is for working Java developers. You know Spring Boot and microservices. You know little or nothing about AI, and that is fine. We start from zero on the AI side and use what you already know on the Java side.
The promise is small and honest. You will get a clear path in four stages. You will build something at every stage. By the end you will have one project you can show to a hiring manager or to your own team.
The whole path takes about 28 weeks if you follow the pace we suggest. That is a suggestion, not a rule. You may go faster or slower, and both are fine.
This is Part 1 of the series. It is the map. Every later part links back here, so you can always see where you are.
Autonomous AI systems represent a structural departure from prompt-and-response interfaces. Rather than evaluating a single inference pass, autonomous agents operate in continuous execution loops: decomposing high-level user intents into discrete actions, interacting with external environments via tools, and evaluating outcomes against target constraints.
Autonomous systems that execute tools without deterministic runtime boundaries introduce systemic risks into production infrastructure. Prompt-level instructions alone cannot guarantee security; external validation gates must inspect every tool payload prior to dispatch.
Retrieval-Augmented Generation (RAG) is essential for grounding agent responses in accurate domain knowledge. However, naive semantic search often fails when dealing with complex multi-file codebase indexing or structured technical documentation.
Relying on a single frontier LLM for every step of a multi-phase agent trajectory is inefficient and prone to latency bottlenecks. As specialized open-weight and proprietary models evolve, the future of production agent engineering lies in dynamic multi-model orchestration with deterministic governance boundaries.
In this deep dive, we examine how to design high-throughput orchestration pipelines, structure runtime fallbacks, and enforce zero-trust tool execution across heterogeneous model fleets.