AI Agents for Java Developers: Setup, Tests, and Context

AI agents can help Java developers configure build files, generate test cases, navigate unfamiliar modules, and make code changes verified by a test run. Getting consistent results means giving each session the project context it needs: the build tool in use, the architecture patterns the team follows, and the decisions already made about how to solve common problems. Tools like fenbs keep those decisions and lessons outside any one chat session, so the same context is available when you switch tools or start a new conversation.

Java projects accumulate conventions quickly. Which logging framework is in use, how exceptions are handled at the boundary layers, whether the team uses records or classes for value types, how tests are organized relative to source — these are not always obvious from the code alone, and an agent that does not know them will make different choices than the ones already established.

Setting up the project context an agent needs

Before asking an agent to make changes, give it a description of the project: the main purpose, the Java version, the build tool (Maven or Gradle), the key frameworks in use (Spring Boot, Quarkus, Micronaut, or others), and the high-level module structure. That description does not need to be long, but it should cover the choices that affect how code is written.

Also describe what the agent should avoid. If the project is migrating away from one logging approach to another, say so. If certain modules are owned by another team and should not be modified, name them. If the project uses a specific pattern for service classes that differs from what a framework tutorial would suggest, write it down.

A useful starting instruction is: "Before you change anything, describe what the relevant module does, what it depends on, and what the test coverage looks like." That forces the agent to demonstrate it has read the code correctly before it makes changes.

Build tools and the agent's environment

Coding agents that run in a terminal can execute Maven or Gradle commands directly. That is useful for verifying that a change compiles and that tests pass, but it also means the agent can run commands that change build state or modify the dependency tree. Be explicit about what it may run: "You may run mvn test on the current module. Do not modify the pom.xml without asking first."

Dependency changes in Java projects have downstream effects. A version bump may resolve one issue and introduce an incompatibility elsewhere. Ask the agent to explain the reason for any proposed dependency change before accepting it, and check the transitive dependency tree if the agent modifies a version.

Agent-assisted test generation

Generating test cases is one of the most consistent uses of coding agents in Java development. Given a service method and its dependencies, an agent can produce a JUnit test class with cases covering the main path, null inputs, boundary values, and common error conditions. That draft is faster than writing from scratch, but it still needs review: check that each case is actually testing the stated condition, that mocks are set up correctly, and that assertions are specific enough to catch a regression.

Ask the agent to explain what each test case is checking before you add it to the test suite. An agent that cannot state the condition a test covers may have generated a case that always passes regardless of the implementation, which is worse than no test at all.

Verifying agent-assisted code changes

For any code change an agent makes, run the full test suite for the affected module before reviewing the diff. If the tests pass, read the diff carefully: does the change do exactly what you asked? Did the agent modify anything outside the scope you gave it? Are there new imports that bring in a library not already in the project?

Pay particular attention to changes near serialization boundaries, transaction annotations, and security-relevant code. Agents sometimes misplace a @Transactional annotation, remove a null check that was intentional, or change a method's visibility in a way that affects how a framework component picks it up. These are the kinds of changes that pass unit tests but cause problems at integration time.

Saving lessons from failed implementations

When an agent's approach fails — a generated migration breaks a constraint, a proposed refactor causes a thread safety issue, a dependency upgrade introduces an incompatibility — record what happened and why. That lesson is valuable context for the next session, and for the next developer who works on the same module.

A useful lesson format: what the agent tried, what the observable failure was, and what the correct approach turned out to be. Keep it short enough to read in thirty seconds. The goal is to prevent the next session from spending an hour on the same dead end.

Working with Spring and framework conventions

Spring projects have conventions that affect how an agent should write every component: whether the project uses annotation-based configuration or Java configuration classes, how transaction boundaries are managed, which beans should be prototype versus singleton scope, and how profiles are used. An agent that does not know these will produce code that is valid Spring but does not match the configuration style already established in the project.

Write the key Spring conventions in the project record before using an agent on Spring-specific code. Include whether the project uses Spring Boot auto-configuration or manual setup, whether there is any custom ApplicationContextInitializer or bespoke PropertySource, and which security configuration is in place. Those details rarely appear in a single file but affect every component the agent touches.

For changes to Spring Security configuration, be especially careful. A configuration change that looks like a simplification may remove a filter or relax an authority check without the change being obvious in the diff. Spring Security applies configurations in a chain where the order and the conditions on each rule determine the effective access policy, so a small change can have a broader effect than the diff suggests. Ask the assistant to explain the security impact of any proposed change, then verify the expected access behavior in a test environment before deploying it.

Navigating an unfamiliar module with an agent

Large Java codebases accumulate patterns that are hard to navigate from a single file: interfaces with multiple implementations, factory classes, and utilities that do similar things in different modules. An assistant can help trace how a request moves through the stack — ask it to describe how a specific handler is reached, or to list the implementations of an interface and explain what each one does.

Give the agent a focused entry point rather than asking it to explain the whole architecture. "How does the RefundService process a failed transaction?" is a question the agent can reason about and that you can verify against the code. A broad question like "explain the architecture" produces a summary that may be accurate at the level of generality but miss the detail you need for the specific change you are planning.

How to do this with fenbs

Start with the most frequently re-explained piece of context in your project — the thing you paste into every new chat. Write it in a fenbs task or Decision, connect your coding agent, and verify that it describes the project correctly from the stored context.

1. Create a free account. Add the Java project with a description of the Java version, build tool, and main frameworks in use.
2. Add current open tasks as fenbs tasks: the feature in progress, the bug to fix, the module to refactor. Include what is known and what is still uncertain.
3. Write architecture decisions in Decisions and rules: module ownership, exception-handling patterns, naming conventions, the logging framework, and any patterns the team is migrating away from.
4. Connect your coding agent at Integrations. Before the next session, ask the agent to read the project context and describe what it understands about the architecture. Correct anything wrong before it writes any code.
5. After each session where an approach was tried and rejected, record a lesson: what was tried, what broke, and what the right approach is. Connected agents read those lessons at the start of similar work.