Automatic Code Validations
A powerful capability of AI coding agents is their ability to run code validation tools, analyze the feedback, and self-correct the code until it meets all defined quality standards. This turns your prompting loop into a smarter, more reliable workflow, where the AI not only writes code but also learns from validation outputs and fixes issues automatically.
Why It Matters
In our experiment, this approach drastically reduced rework, improved code quality, and made the AI a more useful coding assistant. Instead of shipping code with hidden lint errors or poor complexity, the agent could catch and fix these issues in real time, before a human even reviewed it.
How It Works
- The developer prompts the AI to implement a function or feature.
- The AI writes the code and runs validation scripts (linters, formatters, test suites, etc.).
- If a validation fails, the AI uses the feedback from the terminal output as new context and iterates.
- Once all validations pass, the AI can move on and proceed with committing the code.
Example: Cognitive Complexity with Lizard
We use a pre-configured script that runs Lizard and enforces a cognitive complexity limit of 10.
- The AI writes a new function.
- It runs the Lizard script.
- The script returns:
Function X has cognitive complexity of 15 - The AI picks up this feedback and rewrites the function to bring complexity under the limit.
This loop can be applied to many validation tools.
Common Validations Used in the Loop
- Linters (e.g., ESLint)
- Formatters (e.g., Prettier)
- Unit tests (e.g., Jest, Vitest)
- Code complexity analyzers (e.g., Lizard, SonarQube)
- Static analysis tools (e.g., TypeScript compiler, Horusec, Bandit)
Git Integration with Pre-Commit / Pre-Push Hooks
This self-correction loop can also be extended to Git commands. For example:
- You ask the AI:
Stage and commit all changes that pass our validations. - The AI:
- Runs the pre-commit hooks
- Captures the output
- Fixes any issues that arise
- Repeats until validations pass
- Then commits the changes
This ensures that no invalid code ever gets committed, keeping your repo clean and compliant with team rules.