Your app works on your machine. Then a refactor lands, a button stops doing its job, and nobody notices until a user does. Most teams know they need tests. The hard part is deciding which kind goes where, so the suite stays fast and people still trust a red build.

A Java web app testing strategy answers that question layer by layer. In webforJ every layer runs from JUnit in one Maven build, and the newest one tests your views without opening a browser at all.

Hand an AI assistant a Java web project and ask for a new screen. Whether you get working code or an afternoon of fixes depends less on the model than on the framework: how many languages the screen spans, how many ways there are to do the same thing, and whether the assistant can check the real APIs or has to remember them.

The best Java framework for AI coding is the one that leaves the assistant the least room to be wrong. In webforJ the whole UI is plain Java with typed components, so mistakes fail at compile time, and an official AI plugin connects Claude Code, Copilot, Cursor, Gemini and Codex to the real webforJ docs and APIs.

Advanced RAG in Java means fixing retrieval before touching the prompt. Spring AI gives you every stage you need: query rewriting, multi-query expansion, a custom retriever for hybrid search, and a post-processor for reranking, all inside one RetrievalAugmentationAdvisor. Each stage also costs model calls and seconds of waiting, so the real skill is knowing which ones your data needs. This post builds the full pipeline, measures it, and puts it behind a webforJ view that shows what every stage did.

You have a Java app that has been in production for years, a deadline that keeps moving, and a long list of AI tools that all claim they can help. Comparing them by brand does not get you far, because a migration is not one task. It is five or six different jobs, and each one needs a different kind of help.

The best AI tools for Java migration are coding assistants such as Claude Code, GitHub Copilot, Cursor, Gemini CLI and Codex, matched to the job at hand and grounded in the real documentation of the framework you are moving to. That last part decides most of the outcome. With webforJ, the full-stack Java framework, one AI plugin gives those assistants the real docs, project scaffolding and ready-made workflows, so the code they write for your new screens targets APIs that exist.

How far can you get with a Java installable web app in 15 minutes? At Devoxx UK, Stephan Wald, our Head of Development at BASIS International, took that challenge live on stage, while the audience scanned a QR code to open the finished app on their phones. Everything in it is Java, written with webforJ, the full-stack Java framework.

To fill a form with AI in Java, let the user paste free text or upload a file, ask Spring AI for a typed bean with ChatClient and .entity(...), and load that bean into a webforJ form with BindingContext.read. The same Jakarta validation rules that guard manual entry then run on what the model produced, and the user reviews and edits the fields before anything is saved.

This post builds that flow end to end in a webforJ view. The model proposes values, webforJ validates them, and the person stays in control. The complete app, SmartForm, is on GitHub and runs with your own Gemini API key.

The fastest way to build a Spring AI RAG web UI is to write the whole thing in Java with webforJ. The Upload component takes the files, Spring AI reads, splits and embeds them into a VectorStore, a RetrievalAugmentationAdvisor on a ChatClient grounds every answer in the retrieved chunks, and a MarkdownViewer renders the streamed answer. There is no REST layer, no JavaScript and no separate frontend project.

This guide builds a chat-with-your-documents app that way. It is two classes: a Spring service that owns the knowledge base and a webforJ view that talks to it. The complete app, AskDocs, is on GitHub and runs with your own Gemini API key.

An AI assistant can write a Java view in seconds. The slow part comes after: reading the diff, wondering whether that method really exists, and clicking through the screen to see if it behaves. If every change needs that, the speed is gone.

AI coding in Java stays fast when the checks are automatic. webforJ gives you three: a source of truth the assistant reads before it writes, a workflow it has to follow, and tests that fail when it guesses wrong. The tests run in plain JUnit, with no browser, so quality control takes seconds.

You can summarize PDFs with AI in Java by letting the user upload the file in a webforJ view, extracting the text with Spring AI's Tika reader, and sending that text to a chat model. The summary runs on a background thread, so the page stays responsive while the model works. You will build the whole process in three Java files, with no REST controller and no JavaScript. The complete app, Summarize, is on GitHub and runs with your own Gemini API key.

webforJ 26.03 is live! The headline is the automation layer, which lets an AI agent use a webforJ app the way a person does: it reads the page, finds what it needs, and acts, with exactly the permissions of the user it works for. The same layer powers TestBed, new browserless testing built for the way AI coding assistants work. Alongside them, craftforJ picks up component deletion and system-wide undo and redo, BBjShip brings webforJ deployment to BBjServices for BBj developers, an updated DWC Design Kit brings Design System 2.0 to Figma, and important security fixes make this an upgrade we recommend for every app. See the highlights below, and as always, the GitHub release overview has the complete picture.

To build an AI agent in Java that does real work in a web app, give it tools that use the app the way a person does. With Spring AI running the model and webforJ's automation layer as the tools, the agent reads the screen as text, finds a field by its label, types into it, picks from a list, and saves the form. The user watches every step happen in their own browser, and the agent can do nothing the user couldn't.

This is a different thing from a chatbot that answers questions, or from retrieval over your documents. The agent takes a goal such as "ship every pending order for Acme" and works through it in several steps, on the same screens the user works on. This post walks through how such an agent is put together in a Spring Boot app with webforJ.

webforJ, the full-stack Java framework, is the AI Java web framework that covers both sides of the work. While you build, AI coding assistants get the real webforJ API through a plugin, an agent inside your running app writes Java that compiles before you see it, and browserless tests give every change a fast pass or fail. In the app you ship, AI hosts can open your views with MCP Apps, agents can use your UI with exactly the user's permissions, and your own AI features stream into Java components.

All of it is Java, checked by the compiler, and rendered from the server. That is what makes AI output in a webforJ app easy to verify.