Agentic AI is increasingly used during software development to accelerate implementation and reduce repetitive work. When applied carefully it can significantly improve development speed and assist with complex systems such as Drupal. However, the same capabilities can also introduce risks when the generated output is accepted without sufficient understanding or review.
The concept of vibe coding describes a situation where developers rely on generated code that appears plausible but is not properly evaluated. In large Drupal codebases this can lead to fragile implementations and difficult debugging later in the project lifecycle.
This article outlines practical observations about using Agentic AI when building custom Drupal solutions and describes approaches that improve the quality and reliability of generated code.
The primary benefit is increased development speed. Many tasks in Drupal development involve repeating patterns that are already well understood. Agentic AI can accelerate these tasks while still allowing developers to maintain control over the architecture.
Common advantages include:
Despite these advantages, the use of Agentic AI introduces a requirement for stronger review and validation practices.
Agentic AI often performs poorly when requests require both high level design and low level implementation at the same time. When asked to build a complete system from a broad prompt, inconsistencies can appear across files and the resulting code may not align with established Drupal conventions.
A common example occurs when generating Drupal configuration.
For example, nested menu items in Drupal frequently expose limitations in generated output. In repeated attempts to generate nested menu definitions, the following issues often appear:
This happens because the Drupal menu system relies on an understanding of how routes, menu links, and hierarchy interact. Small configuration mistakes can produce subtle errors that are not obvious from reading the YAML files alone.
Similar issues can occur with other declarative Drupal systems:
These areas require familiarity with Drupal conventions rather than only correct syntax.
Agentic AI produces much stronger results when the task is narrowly defined and constrained. Smaller units of work reduce the likelihood of incorrect assumptions and make it easier to verify the output.
Typical examples include:
Another important factor is the presence of good reference code within the project. When the codebase contains consistent patterns, the AI can mirror those patterns effectively.
This is especially useful when extending contributed modules. Pointing the AI toward an existing implementation within a module often leads to more accurate results.
Constraining the scope of the work significantly improves the reliability of generated output. Clear instructions reduce the chance of unnecessary changes or invented patterns.
Effective constraints typically include:
These constraints help maintain consistency within the codebase and reduce unexpected side effects.
A structured workflow improves outcomes when using Agentic AI.
A recommended approach is:
This method ensures that each component can be reviewed and tested in isolation. It also reduces the likelihood that errors will propagate through multiple files.
Agentic AI produces better output when it can follow an established example. Providing explicit references improves both accuracy and consistency.
Useful reference strategies include:
In Drupal development, understanding where similar functionality already exists is often critical. Reference based prompts help ensure that generated code aligns with the framework and the project.
Developers should have a clear understanding of the intended outcome before asking the AI to produce code. This does not prevent collaboration with AI during planning, but it ensures that decisions remain intentional.
Important questions to answer include:
This understanding enables developers to identify incorrect assumptions and evaluate alternative approaches produced by the AI.
Automated tooling is essential when integrating generated code into a production codebase. Validation should rely on objective signals rather than intuition.
Common tools that support this process include:
These tools provide feedback that can highlight incorrect assumptions or incomplete implementations.
Without a clear expectation of the intended outcome, it becomes difficult to evaluate generated code. Code that appears correct may still conflict with Drupal conventions or system behaviour.
The most reliable approach is to ensure that:
Agentic AI should support development rather than replace technical reasoning.
Agentic AI is a valuable addition to modern Drupal development workflows. It can accelerate implementation and reduce the effort required for repetitive tasks. However, effective use requires deliberate structure and careful validation.
Teams that benefit most from Agentic AI tend to follow several consistent practices:
When used in this way, Agentic AI becomes a tool that enhances development rather than introducing uncertainty into the codebase.
