AI Coding Agents and the Silent Explosion of Technical Debt.
AI coding agents are everywhere right now. They promise faster delivery, fewer repetitive tasks, and the ability to ship more features with smaller teams. In many cases, they genuinely deliver on that promise. The problem starts when their output is treated as inherently trustworthy and merged without proper review.
When AI-generated code goes unchallenged, technical debt doesn’t creep in — it accumulates at speed.
On the surface, AI-written code often looks fine. It compiles, tests might pass, and the feature appears to work. That’s usually where the trouble begins. Because it works, it gets merged. Because it gets merged, it becomes someone else’s problem later.
AI tools don’t understand your system in the way your team does. They don’t know why certain architectural decisions were made, which shortcuts are deliberate, or which constraints matter most to the business. They generate plausible solutions, not necessarily appropriate ones. That distinction is critical.
We regularly see AI-generated code that:
- Duplicates existing logic instead of extending it
- Bypasses established abstractions
- Introduces inconsistent naming, structure, or error handling
- Solves the immediate problem in a way that makes future change harder
None of these issues are catastrophic in isolation. The real danger is volume. AI can produce large amounts of code very quickly, which means it can also produce large amounts of poorly aligned code just as fast. What used to take months of gradual entropy can now happen in a single sprint.
This is how teams end up with systems that technically work but are increasingly fragile. Simple changes take longer. Bugs appear in unexpected places. New developers struggle to understand how the system fits together. Eventually, delivery slows down — often faster than it ever sped up.
The mitigation is not complicated, but it does require discipline.
Every piece of AI-generated code should be treated the same way as code written by a junior developer: helpful, fast, and absolutely in need of review. Human review is where context, judgement, and experience come back into the loop.
Proper review ensures that AI output:
- Follows agreed design patterns and architectural principles
- Fits cleanly into the existing codebase
- Uses shared abstractions instead of reinventing them
- Is readable, maintainable, and testable
Humans can ask the questions AI cannot. Is this the right place for this logic? Does this align with where the system is heading? Are we making future work harder for ourselves?
Used well, AI coding agents are a force multiplier. They remove friction and speed up execution. Used without oversight, they become a fast-moving source of technical debt that quietly erodes long-term productivity.
At PHC Digital, we see AI as a tool — not an authority. Speed matters, but so does structure. The teams that get real value from AI are the ones that pair automation with strong engineering practices, clear standards, and consistent human review.
That balance is what keeps velocity sustainable rather than short-lived.