How to Future Proof Your Codebase in the Age of AI

The Problem With AI Code

AI can generate working code quickly. But "working" is a low bar. The code AI generates is often:

• Functional but not elegant
• Correct for the common case, but fragile at the edges
• Difficult to maintain because it lacks clear intent
• Overengineered or underengineered (hard to tell with AI)
• Missing error handling and edge cases

Yet teams are shipping this code to production. The question isn't whether AI-generated code is here—it is. The question is how you ensure it maintains the quality standards your organization needs.

Establish Clear Code Standards

This is more important than ever. With AI generating code, you need explicit, documented standards that your team enforces ruthlessly.

What should your standards cover?

• Naming: Variables, functions, classes should have clear, intention-revealing names. AI tends to generate names like `data`, `item`, `result`. Enforce better.
• Function Size: Smaller functions are easier to test and understand. Set a maximum line count per function.
• Complexity: Cyclomatic complexity matters. Tools like SonarQube measure it. Set a threshold and reject code that exceeds it.
• Documentation: AI-generated code often lacks comments explaining the "why." Require docstrings for public functions.
• Error Handling: AI frequently generates code without proper error handling. Make this explicit in your standards.

Testing is No Longer Optional

With AI generating code, testing becomes your primary defense against bugs. You can't rely on developers understanding the code deeply—you must rely on test coverage.

What coverage do you need? 80-90% line coverage is a minimum. But coverage isn't just about hitting lines—it's about testing edge cases, error conditions, and failure modes.

Structure your tests:

• Unit Tests: Fast, isolated, testing individual functions. These should be the bulk of your tests.
• Integration Tests: Testing how components work together. AI often misses integration points.
• Contract Tests: If you have APIs, ensure they match what consumers expect. AI can break contracts without realizing.
• Scenario Tests: Real-world workflows, not just happy paths. AI struggles with edge cases.

For every PR with AI-generated code, require tests that prove the code works as intended. Don't accept code without tests.

Code Review Gets Harder and More Important

When humans write code, a reviewer can spot logical issues. When AI writes code, the logic might be correct but the intent unclear. Code reviews need to adapt.

Review checklist for AI-generated code:

• Is the intent clear? Does the code do what the PR description claims?
• Are there obvious performance issues? AI can generate O(n²) algorithms when O(n) is available.
• Are edge cases handled? Null checks, empty arrays, off-by-one errors.
• Is error handling comprehensive? What happens when things go wrong?
• Is the code tested? Does the test coverage prove correctness?
• Could this be simpler? AI sometimes overcomplicates simple problems.

Architecture is Your Defense

Good architecture protects you from bad code. With AI generating code at scale, this becomes critical.

Well-defined boundaries: If each module has a clear responsibility and dependencies are managed, bad code in one module doesn't spread.

Dependency injection: Makes code testable, and makes it easy to mock. Insist on it. AI-generated code that follows dependency injection patterns is easier to test.

Contracts over implementation: Define clear interfaces. As long as implementation matches the interface, the details matter less. Enforce this in code review.

Monitoring and observability: You can't test every scenario. So you monitor production. If AI-generated code behaves unexpectedly, you catch it quickly.

Documentation Gets More Important

AI-generated code often lacks context. Why was this architectural choice made? Why this algorithm instead of that one? Without documentation, this knowledge is lost.

What to document:

• Architecture Decision Records: Why did you choose this design? What tradeoffs did you accept?
• Algorithm explanations: If code uses a complex algorithm, document why and how it works.
• Edge case notes: "This handles empty arrays by returning null because..."
• Performance assumptions: "This assumes data is already sorted" or "This runs in O(n log n) time."

Automated Quality Gates

Don't rely on human review alone. Automate as much as possible:

• Static Analysis: SonarQube, Checkmarx. Reject PRs that violate code standards.
• Test Coverage: Require minimum coverage. Reject PRs that decrease it.
• Performance Testing: Run performance benchmarks. Reject PRs that degrade performance significantly.
• Security Scanning: SAST, dependency scanning, container scanning. Build these into CI/CD.
• Complexity Analysis: Automatically measure cyclomatic complexity. Reject code that exceeds thresholds.

Train Your Team on AI Output Evaluation

Developers need to develop judgment about AI-generated code. This requires training:

• What AI does well and what it does poorly
• How to read AI-generated code and spot issues
• How to write effective prompts that generate better code
• What to test more thoroughly because AI is less reliable

How Trostrum Can Help

Building AI-native quality practices is complex. We help teams:

• Develop code standards appropriate for AI-assisted development
• Design comprehensive testing strategies
• Implement automated quality gates and CI/CD pipelines
• Train teams on evaluating AI-generated code
• Build architecture that constrains AI-generated code appropriately

Final Thoughts

AI-generated code is faster. But "fast" is only valuable if the code is maintainable, secure, and correct. Future-proofing your codebase in the age of AI means being intentional about quality, discipline about testing, and smart about where AI adds value.

The organizations that will win aren't those that generate the most code the fastest. They're the ones that generate code that's fast, correct, and maintainable.