Some AI coding problems are prompting problems. Use clearer instructions, provide better context, and the results improve. But other problems persist no matter how good your prompts get. Those are governance problems.
Here are seven signs that your AI coding assistant needs guardrails, not better prompts.
1. The Same Mistake, Every Session
You corrected this yesterday. You corrected it last week. You've corrected it so many times you have a template response. And today, Claude suggested it again.
When corrections don't persist, you don't have a prompting problem. You have a memory problem. The AI isn't learning because it can't learn—each session starts fresh.
2. Ignored Project Conventions
Your CLAUDE.md file clearly states: "Use dependency injection. Never instantiate services directly." Claude generates code that instantiates services directly. It read the instruction. It ignored the instruction.
Documentation is a suggestion. Guardrails are requirements. Without enforcement, conventions are aspirational.
3. Scope Creep on Every Request
You asked for a simple utility function. You got a utility function, a helper class, a configuration system, and "some improvements to the surrounding code." Every small request expands into a large change.
AI assistants optimize for perceived helpfulness. Without boundaries, they'll "help" by touching code you didn't ask them to touch. Guardrails define scope and enforce it.
4. Partial Implementations That Look Complete
The code looks done. The tests pass. You ship it. A week later, you discover the error handling only covers the happy path. The edge cases aren't forgotten—they were never implemented.
AI assistants are confident by default. They present partial work as complete work. Without verification that checks for completeness, you're trusting confidence, not correctness.
5. Test Coverage That Tests the Wrong Things
You asked for tests. You got tests. Coverage metrics look good. But the tests test what's easy to test, not what's important to test. The critical paths are untouched.
Spec-driven verification defines what must be tested, not just that testing happened. Without it, you're measuring activity, not quality.
6. Silent Integration Failures
The new code works in isolation. It passes its unit tests. When integrated with the rest of the system, something breaks—but not obviously. A feature degrades. Performance drops. An edge case that used to work now fails.
AI assistants work on the code you show them. They can't see the ripple effects of their changes. Guardrails that verify integration catch what isolated testing misses.
7. Regression Déjà Vu
You fixed this bug two weeks ago. The AI just reintroduced it. Not a similar bug—the exact same bug. Because it doesn't know the bug ever existed, or that you fixed it, or why the fix mattered.
Without learning that compounds across sessions, every session is equally likely to reintroduce old bugs. Guardrails with pattern memory remember what went wrong and prevent recurrence.
What Guardrails Actually Look Like
Guardrails aren't better prompts. They're infrastructure that enforces standards regardless of how the AI interprets your instructions.
- Multi-layer verification that checks code before it's committed
- Spec-driven completeness verification that knows when something's missing
- Pattern memory that persists corrections across sessions
- Scope enforcement that prevents unwanted changes
- Integration testing that catches ripple effects
CleanAim® provides this infrastructure. The AI can't claim "done" until the system confirms it. Patterns compound across sessions. Corrections become permanent. The guardrails hold.