AI-assisted debugging feels magical the first time you use it: you paste in a failing test, get back a patch, and suddenly everything is green.
And yet, after a few weeks, a pattern emerges:
the system works, but it is subtly worse than before.
More checks.
More wrappers.
Blurred boundaries.
Weaker guarantees.
Nothing is obviously broken. Yet.
This is not because the AI is “bad at coding.” It is because it is very good at optimizing the wrong objective.
The local optimiser problem
AI debugging tools are fundamentally local optimizers.
They are not reasoning about:
- architectural coherence
- invariant preservation
- security posture
- operational responsibility
They are reasoning about: “What change makes this test pass?”
A failing test is treated as a symptom to suppress, not as a signal that the system entered an invalid state. This difference matters.
Human engineers, especially experienced ones, tend to ask: What assumption was violated for this test to fail?
AI systems tend to ask: What code change produces the expected output for this input?
The result is a class of fixes that are locally correct and globally corrosive.
Seven recurring failure patterns
Over time, the same patterns show up again and again.
1. Invalid states become “handled cases”
Instead of restoring invariants, the fix expands exception handling or fallback logic. The system stops failing loudly and starts tolerating states that should never occur.
2. Configuration leaks into code
Temporary constants, flags, or environment checks appear in the execution path because they are the fastest way to influence behavior.
3. Security is treated as an inconvenience
Validation is weakened, authorization is deferred, logging becomes indiscriminate—often with the promise that “ops will handle it later.”
They rarely do.
4. Defensive checks proliferate
Every layer starts checking everything, because the model cannot reliably reason about upstream guarantees. Validation loses its owner.
5. Separation of concerns erodes
Persistence, transport, and test semantics bleed into core logic, because that is where the assertion needs to pass.
6. DRY quietly dies
New wrappers appear that are almost the same as existing ones. Small differences accumulate. Behavior diverges.
7. The explanation sounds right, but isn’t
The most dangerous fixes are the ones accompanied by confident but incorrect causal narratives. They work—until they don’t.
Why this feels familiar
If this all sounds familiar, it should.
This is exactly how junior engineers under time pressure behave:
- optimize for visible success
- satisfy the test or ticket
- defer systemic cleanup
- rely on plausibility instead of proof
The difference is speed. AI does this instantly and repeatedly.
How to work with AI debugging without paying the price
The solution is not to stop using AI.
It is to treat AI-generated fixes as raw material, not finished work.
In practice:
- Refactor after the fix, explicitly restoring abstraction boundaries
- Remove duplicated checks and reassign validation to clear contract points
- Consolidate helpers to re-establish DRY
- Move configuration and security concerns back to their proper layers
- Ask the AI (or yourself) to explain which invariant was restored—and reject the fix if it cannot
A useful trick is to ask: “Write down the assumptions this fix relies on.”
If those assumptions are not already guaranteed elsewhere in the system, the fix is incomplete.
The real shift
AI-assisted debugging does not eliminate engineering judgment.
It compresses the time between decisions.
That makes discipline more important, not less.
The danger is not that AI writes bad code.
The danger is that it writes plausible code that quietly changes what your system means.
Green tests are not the same thing as a healthy system.
Some AI-debugging rules of thumb
Below is a pragmatic checklist for turning “tests are green” into “the system is healthy”. AI can get you from failing tests to green builds fast. The cost is that it often optimizes for the most local objective: “make this assertion pass.” What follows is a set of cleanup rules I use after AI-assisted debugging, so the fix doesn’t quietly degrade architecture, operability, or security.
These are not “coding standards.” They are post-fix hygiene rules: the things I explicitly check and refactor once the smoke clears.
Rule 1. Name the invariant and contract boundary
Rule 2. Remove catch-alls and “green-by-suppression” fallbacks
Rule 3. Centralize configuration; delete hidden defaults
Rule 4. Restore security posture validation, authorisation, certificate handling
Rule 5. Assign validation ownership; delete duplicated checks
Rule 6. Re-establish separation of concerns (transport/persistence/policy)
Rule 7. Delete needless wrappers; consolidate helpers to restore DRY
Rule 8. Ensure error handling changes outcomes, e.g. propagate or compensate
Rule 9. Normalise patterns across the codebase
Rule 10. Refactor until the patch looks intentional
Rule 11. Document assumptions and reread skeptically
Rule 12. Add at least one invariant-level test
Controlled AI services 