Do You Smell That? Hidden Technical Debt in AI Development

“smell” them at first. In practice, code smells are warning signs that suggest future problems. The code may match today, but its structure hints that it is going to change into hard to keep up, test, scale, or secure. Smells are not necessarily bugs; they’re indicators of design debt and long-term product risk.

These smells typically manifest as slower delivery and better change risk, more frequent regressions and production incidents, and fewer reliable AI/ML outcomes, often driven by leakage, bias, or drift that undermines evaluation and generalization.

The Path from Prototype to Production

Most phases in the event of knowledge/AI products can vary, but they sometimes follow an identical path. Typically, we start with a prototype: an idea first sketched, followed by a small implementation to show value. Tools like Streamlit, Gradio, or n8n may be used to present a quite simple concept using synthetic data. In these cases, you avoid using sensitive real data and reduce privacy and security concerns, especially in large, privacy‑sensitive, or highly regulated firms.

Later, you progress to the PoC, where you utilize a sample of real data and go deeper into the features while working closely with the business. After that, you progress toward productization, constructing an MVP that evolves as you validate and capture business value.

More often than not, prototypes and PoCs are built quickly, and AI makes it even faster to deliver them. The issue is that this code rarely meets production standards. Before it may well be robust, scalable, and secure, it normally needs refactoring across engineering (structure, readability, testing, maintainability), security (access control, data protection, compliance), and ML/AI quality (evaluation, drift monitoring, reproducibility).

Typical smells you see … or not 🫥

This hidden technical debt (often visible as code smells) is straightforward to overlook when teams chase quick wins, and “vibe coding” can amplify it. In consequence, you’ll be able to run into issues akin to:

• Duplicated code: same logic copied in multiple places, so fixes and changes change into slow and inconsistent over time.
• God script / god function: one huge file or function does every part, making the system hard to know, test, review, and alter safely because every part is tightly coupled. This violates the Single Responsibility Principle [1]. Within the agent era, the “god agent” pattern shows up, where a single agent entrypoint handles routing, retrieval, prompting, actions, and error handling multi function place.
• Rule sprawl: behavior grows into long if/elif chains for brand new cases and exceptions, forcing repeated edits to the identical core logic and increasing regressions. This violates the Open–Closed Principle (OCP): you retain modifying the core as a substitute of extending it [1]. I’ve seen this early in agent development, where intent routing, lead-stage handling, country-specific rules, and special-case exceptions quickly accumulate into long conditional chains.
• Hard-coded values: paths, thresholds, IDs, and environment-specific details are embedded in code, so changes require code edits across multiple places as a substitute of straightforward configuration updates.
• Poor project structure (or folder layout): application logic, orchestration, and platform configuration live together, blurring boundaries and making deployment and scaling harder.
• Hidden unwanted effects: functions do extra work you don’t expect (mutating shared state, writing files, background updates), so outcomes depend upon execution order and bugs change into hard to trace.
• Lack of tests: there are not any automated checks to catch drift after code, prompt, config, or dependency changes, so behavior can change silently until systems break. (Sadly, not everyone realizes that tests are low-cost, and bugs aren’t).
• Inconsistent naming & structure: makes the code harder to know and onboard others to, slows reviews, and makes maintenance depend upon the unique creator.
• Hidden/overwritten rules: behavior will depend on untested, non-versioned, or loosely managed inputs akin to prompts, templates, settings, etc. In consequence, behavior can change or be overwritten without traceability.
• Security gaps (missing protections): Things like input validation, permissions, secret handling, or PII controls are sometimes skipped in early stages.
• Buried legacy logic: old code akin to pipelines, helpers, utilities, etc. stays scattered across the codebase long after the product has modified. The code becomes harder to trust since it encodes outdated assumptions, duplicated logic, and dead paths that also run (or quietly rot) in production.
• Blind operations (no alerting / no detection): failures aren’t noticed until a user complains, someone manually checks the CloudWatch logs, or a downstream job breaks. Logs may exist, but no one is actively monitoring the signals that matter, so incidents can run unnoticed. This often happens when external systems change outside the team’s control, or when too few people understand the system or the info.
• Leaky integrations: business logic will depend on specific API/SDK details (field names, required parameters, error codes), so small vendor changes force scattered fixes across the codebase as a substitute of 1 change in an adapter. This violates the Dependency Inversion Principle (DIP) [1].
• Environment drift (staging ≠ production): teams have dev/staging/pro, but staging will not be truly production-like: different configs, permissions, or dependencies, so it creates false confidence: every part looks high-quality before release, but real issues only appear in prod (often ending in a rollback).

And the list goes on… and on. 

The issue isn’t that prototypes are bad. The issue is the gap between prototype speed and production responsibility, when teams, for one reason or one other, don’t spend money on the practices that make systems reliable, secure, and capable of evolve.

It’s also useful to increase the concept of “code smells” into model and pipeline smells: warning signs that the system could also be producing confident but misleading results, even when aggregate metrics look great. Common examples include fairness gaps (subgroup error rates are consistently worse), spillover/leakage (evaluation by chance includes future or relational information that won’t exist at decision time, generating dev/prod mismatch [7]), or/and multicollinearity (correlated features that make coefficients and explanations unstable). These aren’t academic edge cases; they reliably predict downstream failures like weak generalization, unfair outcomes, untrustworthy interpretations, and painful production drops.

If every developer independently solves the identical problem otherwise (and not using a shared standard), it’s like having multiple remotes (each with different behaviors) for a similar TV. Software engineering principles still matter within the vibe-coding era. They’re what make code reliable, maintainable, and protected to make use of as the muse for real products. 

Now, the sensible query is find out how to reduce these risks without slowing teams down.

Why AI Accelerates Code Smells

AI code generators don’t routinely know what matters most in your codebase. They generate outputs based on patterns, not your product or business context. Without clear constraints and tests, you’ll be able to find yourself with five minutes of “code generation” followed by 100 hours of debugging ☠️. 

Used carelessly, AI may even make things worse:

• It oversimplifies or removes necessary parts.
• It adds noise: unnecessary or duplicated code and verbose comments.
• It loses context in large codebases (lost in the center behavior)

A recent MIT Sloan article notes that generative AI can speed up coding, but it may well also make systems harder to scale and improve over time when fast prototypes quietly harden into production systems [4].

Either way, refactors aren’t low-cost, whether the code was written by humans or produced by misused AI, and the price normally shows up later as slower delivery, painful maintenance, and constant firefighting. In my experience, each often share the identical root cause: weak software engineering fundamentals.

A few of the worst smells aren’t technical in any respect; they’re organizational. Teams may minor debt 😪 since it doesn’t hurt immediately, however the hidden cost shows up later: ownership and standards don’t scale. When the unique authors leave, get promoted, or just move on, poorly structured code gets handed to another person 🫩 without shared conventions for readability, modularity, tests, or documentation. The result’s predictable: maintenance turns into archaeology, delivery slows down, risk increases, and the one that inherits the system often inherits the blame too.

Checklists: a summarized list of recommendations

This can be a complex topic that advantages from senior engineering judgment. A checklist won’t replace platform engineering, application security, or experienced reviewers, nevertheless it can reduce risk by making the fundamentals consistent and harder to skip.

1. The missing piece: “Problem-first” design

A “design-first / problem-first” mindset signifies that before constructing an information product or AI system (or repeatedly piling features into prompts or if/else rules), you clearly define the issue, constraints, and failure modes. And this will not be only about product design (what you construct and why), but in addition software design (the way you construct it and the way it evolves).

It’s also necessary to do not forget that technology teams (AI/ML engineers, data scientists, QA, cybersecurity, and platform professionals) are a part of the business, not a separate entity. Too often, highly technical roles are seen as disconnected from broader business concerns. This stays a challenge for some business leaders, who may view technical experts as know-it-alls somewhat than professionals (not all the time true) [2].

2. Code Guardrails: Quality, Security, and Behavior Drift Checks

In practice, technical debt grows when quality will depend on people “remembering” standards. Checklists make expectations explicit, repeatable, and scalable across teams, but automated guardrails go further: you’ll be able to’t merge code into production unless the fundamentals are true. This guarantees a minimum baseline of quality and security on every change.

Automated checks help stop probably the most common prototype problems from slipping into production. Within the AI era, where code may be generated faster than it may well be reviewed, code guardrails act like a seatbelt by enforcing standards consistently. A practical way is to run checks as early as possible, not only in CI. For instance, Git hooks, especially pre-commit hooks, can run validations before code is even committed [5]. Then CI pipelines run the total suite on every pull request, and branch protection rules can require those checks to pass before a merge is allowed, ensuring code quality is enforced even when standards are skipped.

A solid baseline normally includes:

• Linters (e.g., ruff): enforces consistent style and catches common issues (unused imports, undefined names, suspicious patterns).
• Tests (e.g., pytest): prevents silent behavior changes by checking that key functions and pipelines still behave as expected after code or config edits.
• Secrets scanning (e.g., Gitleaks): blocks accidental commits of tokens, passwords, and API keys (often hardcoded in prototypes).
• Dependency scanning (e.g., Dependabot / OSV): flags vulnerable packages early, especially when prototypes pull in libraries quickly.
• LLM evals (e.g., prompt regression): if prompts and model settings affect behavior, treat them like code by testing inputs and expected outputs to catch drift [6].

That is the short list, but teams often add additional guardrails as systems mature, akin to type checking to catch interface and “None” bugs early, static security evaluation to flag dangerous patterns, coverage and complexity limits to forestall untested code, and integration tests to detect breaking changes between services. Many also include infrastructure-as-code and container image scanning to catch insecure cloud setting, plus data quality and model/LLM monitoring to detect schema and behavior drift, amongst others.

How this helps

AI-generated code often includes boilerplate, leftovers, and dangerous shortcuts. Guardrails like linters (e.g., Ruff) catch predictable issues fast: messy imports, dead code, noisy diffs, dangerous exception patterns, and customary Python footguns. Scanning tools help prevent accidental secret leaks and vulnerable dependencies, and tests and evals make behavior changes visible by running test suites and prompt regressions on every pull request before production. The result is quicker iteration with fewer production surprises.

Release guardrails

Beyond pull request to production (PR) checks, teams also use a staging environment as a lifecycle guardrail: a production-like setup with controlled data to validate behavior, integrations, and value before release.

3. Human guardrails: shared standards and explainability

Good engineering practices akin to code reviews, pair programming, documentation, and shared team standards reduce the risks of AI-generated code. A typical failure mode in vibe coding is that the creator can’t clearly explain what the code does, how it really works, or why it should work. Within the AI era, it’s essential to articulate intent and value in plain language and document decisions concisely, somewhat than counting on verbose AI output. This isn’t about memorizing syntax; it’s about design, good practices, and a shared learning discipline, since the only constant is change.

4. Responsible AI by Design 

Guardrails aren’t only code style and CI checks. For AI systems, you furthermore may need guardrails across the total lifecycle, especially when a prototype becomes an actual product. A practical approach is a “Responsible AI by Design” checklist covering minimum controls from data preparation to deployment and governance.

At a minimum, it should include:

• Data preparation: privacy protection, data quality control, bias/fairness checks.
• Model development: business alignment, explainability, robustness testing.
• Experiment tracking & versioning: reproducibility through dataset, code, and model version control.
• Model evaluation: stress testing, subgroup evaluation, uncertainty estimation where relevant.
• Deployment & monitoring: monitor drift/latency/reliability individually from business KPIs; define alerts and retraining rules.
• Governance & documentation: audit logs, clear ownership, and standardized documentation for approvals, risk evaluation, and traceability.

The one-pager of figure 1 is just a primary step. Use it as a baseline, then adapt and expand it along with your expertise and your team’s context.

The one-pager of figure 1 is just a primary step. Use it as a baseline, then adapt and expand it along with your expertise and your team’s context.

5. Adversarial testing

There may be extensive literature on adversarial inputs. In practice, teams can test robustness by introducing inputs (in LLMs and classic ML) the system never encountered during development (malformed payloads, injection-like patterns, extreme lengths, weird encodings, edge cases). The secret is cultural: adversarial testing should be treated as a standard a part of development and application security, not a one-off exercise.

This emphasizes that analysis will not be a single offline event: teams should validate models through staged release processes and repeatedly maintain evaluation datasets, metrics, and subgroup checks to catch failures early and reduce risk before full rollout [8].

Conclusion

A prototype often looks small: a notebook, a script, a demo app. But once it touches real data, real users, and real infrastructure, it becomes a part of a dependency graph, a network of components where small changes can have a surprising blast radius.

This matters in AI systems since the lifecycle involves many interdependent moving parts, and teams rarely have full visibility across them, especially in the event that they don’t plan for it from the start. That lack of visibility makes it harder to anticipate impacts, particularly when third-party data, models, or services are involved.

What this often includes:

• Software dependencies: libraries, containers, construct steps, base images, CI runners.
• Runtime dependencies: downstream services, queues, databases, feature stores, model endpoints.
• AI-specific dependencies: data sources, embeddings/vector stores, prompts/templates, model versions, fine-tunes, RAG knowledge bases.
• Security dependencies: IAM/permissions, secrets management, network controls, key management, and access policies.
• Governance dependencies: compliance requirements, auditability, and clear ownership and approval processes.

For the business, this will not be all the time obvious. A prototype can look “done” since it runs once and produces a result, but production systems behave more like living things: they interact with users, data, vendors, and infrastructure, they usually need continuous maintenance to remain reliable and useful. The complexity of evolving these systems is straightforward to underestimate because much of it’s invisible until something breaks.

That is where quick wins may be misleading. Speed can hide coupling, missing guardrails, and operational gaps that only show up later as incidents, regressions, and dear rework. This text inevitably falls in need of covering every part, however the goal is to make that hidden complexity more visible and to encourage a design-first mindset that scales beyond the demo.

References

[1] Martin, R. C. (2008). . Prentice Hall. 

[2] Hunt, A., & Thomas, D. (1999). . Addison-Wesley.

[3] Kanat-Alexander, M. (2012). . O’Reilly Media. 

[4] Anderson, E., Parker, G., & Tan, B. (2025, August 18). (Reprint 67110). MIT Sloan Management Review.

[5] iosutron. (2023, March 23). Lost in tech. WordPress. 

[6] Arize AI. (n.d.). . Retrieved January 10, 2026, from Arize AI. 

[7] Gomes-Gonçalves, E. (2025, September 15). . Towards Data Science. Retrieved January 11, 2026, from Towards Data Science.

[8] Shankar, S., Garcia, R., Hellerstein, J. M., & Parameswaran, A. G. (2022, September 16). . arXiv:2209.09125. Retrieved January 11, 2026, from arXiv.