Agentic AI tools such as Claude can accelerate many aspects of software development. However, when these systems interact with Apache Maven™, they often encounter fundamental difficulties. The root cause lies in Maven’s unique life cycle–based execution model, which differs significantly from build tools based on directed acyclic graphs (DAG) such as Gradle.
Modern AI assistants are trained to optimize workflows through small, targeted actions. When asked to validate a code change, they naturally try to issue the narrowest command possible. This expectation maps well to tools that use DAG-based execution, where dependencies1 are rebuilt only when necessary.
Maven, by contrast, is organized around sequential life cycle phases (clean → validate → compile → test → verify → install). While this approach ensures correctness when executed from the project root, it does not naturally align with the AI’s assumption that it can target specific modules or tests individually. As a result, AI-driven commands often fail or even produce misleading results.
The examples that I’m presenting here can be grouped into false negatives and false positives. They present the result of working with Claude Code on a relatively small Java code base consisting of a handful of Maven modules with a parent POM file in the project root. In my experience the AI often creates commands that lead it to form an incorrect picture of the project state - either assuming something is broken when it is not, or assuming everything works when in fact it does not.
A false negative example is targeted test execution from the root without scoping. Asked to run a specific test, or even to just verify the changes it just made, an agent might do:
mvn test -Dtest=ApiTestFrom the project root, Maven will execute the test phase in every module.
Because ApiTest exists only in modules/api, the Maven Surefire Plugin treats the other modules as having “no specified tests” and - by default - fails the build.
The AI interprets this as a genuine defect and begins searching for a non‑existent problem until it is told to scope the command to the module that contains the test, for example:
mvn test -Dtest=ApiTest -pl modules/apiHowever this may now produce another false negative because dependent modules will not be compiled, which might either cause compilation issues in the test or run tests on unfixed code causing a test failure although the code was already fixed.
Maven experts will suggest combining the -am flag (short for --also-make which will also build modules depended on by the modules in the -pl list) together with Surefire’s -Dsurefire.failIfNoSpecifiedTests=false flag to address this, for example:
mvn test -Dtest=ApiTest -pl modules/api -am -Dsurefire.failIfNoSpecifiedTests=falseWhile this will finally do the right thing, only few people I’ve met know about this combination, and it requires very carefully crafted prompting for an AI system to apply it correctly.
False negatives wastes compute cycles and tokens, making the process more expensive without producing real progress.
Let’s look at another variant of the situation with the command that selects the test from a particular module:
mvn test -Dtest=ApiTest -pl modules/apiWhat if we’ve changed something in modules/core and this change would break ApiTest?
Since the build command only requests to build module/api, Maven will not rebuild modules/core, even though it has changed.
This can results in a false positive: if code in the core module was broken by changes but not recompiled, then ApiTest may still pass because it runs against stale compile outputs.
The build shows green although the code has a bug.
An AI will assume everything is fine and may respond with high confidence.
For example, Claude might conclude:
🎉 Perfect! All tests passed successfully, your change is good to go.
In reality the broken code is about to be shipped.
Another false positive arises from the split between the Maven Surefire and Maven Failsafe plugins. Unit tests are executed by Surefire in the test phase, while integration tests are executed by Failsafe in the verify phase. AI tools often attempt:
mvn test -Dtest=IntegrationTestThis silently skips integration tests.
The problem is twofold: the AI uses mvn test instead of the correct mvn verify, and it also specifies -Dtest instead of -Dit.test.
As a result, integration tests are never run, yet the AI may believe that all tests are passing.
This is more severe, as a developer leaning too heavily on AI might ship broken code.
When told that integration tests have to be executed with Failsafe, AI sometimes resorts to executing the plugin directly, for example:
mvn failsafe:integration-test failsafe:verifyBut this just exaggerates the problem, because now Maven only executes that specific goal without running any of the upstream phases, even within that module.
False positives are especially dangerous because they mislead both the AI and the developer into believing that everything is working when it is not. The AI will confidently announce success, and a developer relying too heavily on such feedback may merge or release broken code. This not only undermines trust in the AI assistant but also creates direct costs: wasted compute resources, wasted tokens, and the potential expense of shipping faulty software into production.
All of these examples point back to the same root cause. AI tools are designed to do the minimal work needed to answer a question or validate a change. Maven, on the other hand, requires full life cycle execution based on clean project state for everything to work correctly. Skipping phases or scoping commands too narrowly almost always leads to incorrect results. This is why developers often resort to heavy-handed commands such as:
mvn clean installThis discards all previous work and executes the full build from scratch, ensuring consistency but at the cost of time and resources - even if many modules or phases are unchanged. The life cycle model prioritizes determinism and correctness, but it clashes with the optimization instincts of AI agents.
It is also worth noting that Maven’s strict life cycle model is quite unique in the world of build tools. Most other popular systems follow a dependency graph–based approach. Well-known examples include Gradle, Bazel, Make, and Cargo, all of which determine what needs to be rebuilt based on a graph and file changes.
DAG-based build tools like Gradle operate differently. Instead of enforcing linear phases, they determine what actually needs to be rebuilt based on task dependency relationships and file changes. This incremental, task dependency-aware approach makes them a more natural fit for AI workflows.
Consider the earlier examples. Running a single test with Gradle can be done as:
./gradlew test --tests ApiTestGradle will find all test sets that have a test matching the specified name. It will then identify all downstream tasks required to run those tests. Any tasks whose outputs are already up to date in the workspace are skipped, and tasks with outputs available in the build cache may also be skipped. This ensures that dependent modules, such as core, are recompiled only if necessary, and avoids redundant work. Integration tests are triggered through the same test task mechanism, without requiring knowledge of plugin boundaries like Surefire versus Failsafe. Unlike Maven, there is no need to discard all prior work with a clean install to be sure the state is correct. This makes the CLI experience with Gradle much clearer not only for AI agents but also for human developers.
This predictable and incremental model means that AI commands map more directly to correct builds. The AI can issue focused commands with confidence that the right scope will be rebuilt and tested, reducing both wasted effort and the risk of false signals. And because DAG-based tools are common across ecosystems - from Cargo in Rust to Make in C/C++ - the AI can transfer learned patterns more effectively. Maven, by contrast, remains the outlier.
While AI tools find Gradle easier to execute, the situation reverses when they must author or modify build logic. Maven’s XML-based configuration model has remained stable for years, which means there are many valid examples in the public domain for an AI to learn from. The structure is consistent, and the semantics of the POM rarely change, allowing an AI to produce correct edits most of the time.
When it comes to editing build files, Gradle presents a different challenge for AI agents.
There are two distinct DSLs - Groovy and Kotlin - with a third, the Declarative Configuration Language (DCL), currently in development.
In addition, Gradle frequently evolves its best practices, including the move from allprojects to local build plugins, ongoing API changes, and the introduction of the Provider API.
These shifts make it difficult for an AI model to determine the correct syntax and structure for a given Gradle version.
As a result, while AI may execute Gradle commands more effectively, it struggles far more when asked to author or fix Gradle build scripts.
Maven’s life cycle model requires full sequential execution to produce correct results, while DAG-based tools such as Gradle determine and execute only what is necessary. This makes Gradle’s execution model more natural for AI systems that aim to minimize work while maintaining correctness. Maven’s stable XML configuration still offers an advantage for generating or maintaining build logic, but when it comes to running builds, DAG-based tools align far better with how both AI agents and developers think about incremental work.
If you’re navigating challenges with Maven or Gradle - or looking to improve your build automation workflows - I offer consulting and guidance to help teams modernize and optimize their builds. Learn more at britter.dev/services.
I’m talking about task dependencies, such as “creating the jar, depends on compiling the Java code first”, not binary dependencies that build tools pull from repositories such as Maven Central. ↩