Do You Smell That? Hidden Technical Debt in AI Development

“odor” them at first. In follow, code smells are warning indicators that counsel future issues. The code may fit at this time, however its construction hints that it’s going to turn out to be onerous to take care of, check, scale, or safe. Smells are not essentially bugs; they’re indicators of design debt and long-term product threat.

These smells usually manifest as slower supply and better change threat, extra frequent regressions and manufacturing incidents, and fewer dependable AI/ML outcomes, typically pushed by leakage, bias, or drift that undermines analysis and generalization.

The Path from Prototype to Manufacturing

Most phases within the improvement of knowledge/AI merchandise can range, however they often observe an analogous path. Sometimes, we begin with a prototype: an concept first sketched, adopted by a small implementation to display worth. Instruments like Streamlit, Gradio, or n8n can be utilized to current a quite simple idea utilizing artificial knowledge. In these circumstances, you keep away from utilizing delicate actual knowledge and cut back privateness and safety issues, particularly in giant, privateness‑delicate, or extremely regulated firms.

Later, you progress to the PoC, the place you utilize a pattern of actual knowledge and go deeper into the options whereas working intently with the enterprise. After that, you progress towards productization, constructing an MVP that evolves as you validate and seize enterprise worth.

More often than not, prototypes and PoCs are constructed rapidly, and AI makes it even quicker to ship them. The issue is that this code not often meets manufacturing requirements. Earlier than it may be sturdy, scalable, and safe, it often wants refactoring throughout engineering (construction, readability, testing, maintainability), safety (entry management, knowledge safety, compliance), and ML/AI high quality (analysis, drift monitoring, reproducibility).

Typical smells you see … or not 🫥

This hidden technical debt (typically seen as code smells) is straightforward to miss when groups chase fast wins, and “vibe coding” can amplify it. In consequence, you’ll be able to run into points resembling:

• Duplicated code: similar logic copied in a number of locations, so fixes and adjustments turn out to be gradual and inconsistent over time.
• God script / god operate: one big file or operate does every thing, making the system onerous to know, check, assessment, and alter safely as a result of every thing is tightly coupled. This violates the Single Duty Precept [1]. Within the agent period, the “god agent” sample reveals up, the place a single agent entrypoint handles routing, retrieval, prompting, actions, and error dealing with multi functional place.
• Rule sprawl: conduct grows into lengthy if/elif chains for brand spanking new circumstances and exceptions, forcing repeated edits to the identical core logic and growing regressions. This violates the Open–Closed Precept (OCP): you retain modifying the core as an alternative of extending it [1]. I’ve seen this early in agent improvement, the place intent routing, lead-stage dealing with, country-specific guidelines, and special-case exceptions rapidly accumulate into lengthy conditional chains.
• Laborious-coded values: paths, thresholds, IDs, and environment-specific particulars are embedded in code, so adjustments require code edits throughout a number of locations as an alternative of straightforward configuration updates.
• Poor mission construction (or folder structure): utility logic, orchestration, and platform configuration dwell collectively, blurring boundaries and making deployment and scaling tougher.
• Hidden unintended effects: capabilities do further work you don’t anticipate (mutating shared state, writing information, background updates), so outcomes depend upon execution order and bugs turn out to be onerous to hint.
• Lack of exams: there aren’t any automated checks to catch drift after code, immediate, config, or dependency adjustments, so conduct can change silently till methods break. (Sadly, not everybody realizes that exams are low cost, and bugs should not).
• Inconsistent naming & construction: makes the code tougher to know and onboard others to, slows evaluations, and makes upkeep depend upon the unique creator.
• Hidden/overwritten guidelines: conduct relies on untested, non-versioned, or loosely managed inputs resembling prompts, templates, settings, and so on. In consequence, conduct can change or be overwritten with out traceability.
• Safety gaps (lacking protections): Issues like enter validation, permissions, secret dealing with, or PII controls are sometimes skipped in early phases.
• Buried legacy logic: previous code resembling pipelines, helpers, utilities, and so on. stays scattered throughout the codebase lengthy after the product has modified. The code turns into tougher to belief as a result of it encodes outdated assumptions, duplicated logic, and lifeless paths that also run (or quietly rot) in manufacturing.
• Blind operations (no alerting / no detection): failures aren’t seen till a person complains, somebody manually checks the CloudWatch logs, or a downstream job breaks. Logs could exist, however no one is actively monitoring the indicators that matter, so incidents can run unnoticed. This typically occurs when exterior methods change exterior the group’s management, or when too few individuals perceive the system or the information.
• Leaky integrations: enterprise logic relies on particular API/SDK particulars (area names, required parameters, error codes), so small vendor adjustments pressure scattered fixes throughout the codebase as an alternative of 1 change in an adapter. This violates the Dependency Inversion Precept (DIP) [1].
• Surroundings drift (staging ≠ manufacturing): groups have dev/staging/professional, however staging isn’t really production-like: completely different configs, permissions, or dependencies, so it creates false confidence: every thing seems high quality earlier than launch, however actual points solely seem in prod (typically ending in a rollback).

And the record goes on… and on. 

The issue isn’t that prototypes are dangerous. The issue is the hole between prototype pace and manufacturing accountability, when groups, for one cause or one other, don’t put money into the practices that make methods dependable, safe, and in a position to evolve.

It’s additionally helpful to increase the concept of “code smells” into mannequin and pipeline smells: warning indicators that the system could also be producing assured however deceptive outcomes, even when mixture metrics look nice. Frequent examples embrace equity gaps (subgroup error charges are persistently worse), spillover/leakage (analysis unintentionally contains future or relational data that gained’t exist at determination time, producing dev/prod mismatch [7]), or/and multicollinearity (correlated options that make coefficients and explanations unstable). These aren’t tutorial edge circumstances; they reliably predict downstream failures like weak generalization, unfair outcomes, untrustworthy interpretations, and painful manufacturing drops.

If each developer independently solves the identical drawback differently (with no shared commonplace), it’s like having a number of remotes (every with completely different behaviors) for a similar TV. Software program engineering rules nonetheless matter within the vibe-coding period. They’re what make code dependable, maintainable, and protected to make use of as the inspiration for actual merchandise. 

Now, the sensible query is methods to cut back these dangers with out slowing groups down.

Why AI Accelerates Code Smells

AI code turbines don’t routinely know what issues most in your codebase. They generate outputs based mostly on patterns, not your product or enterprise context. With out clear constraints and exams, you’ll be able to find yourself with 5 minutes of “code technology” adopted by 100 hours of debugging ☠️. 

Used carelessly, AI may even make issues worse:

• It oversimplifies or removes necessary components.
• It provides noise: pointless or duplicated code and verbose feedback.
• It loses context in giant codebases (misplaced within the center conduct)

A latest MIT Sloan article notes that generative AI can pace up coding, however it will probably additionally make methods tougher to scale and enhance over time when quick prototypes quietly harden into manufacturing methods [4].

Both approach, refactors aren’t low cost, whether or not the code was written by people or produced by misused AI, and the associated fee often reveals up later as slower supply, painful upkeep, and fixed firefighting. In my expertise, each typically share the identical root trigger: weak software program engineering fundamentals.

A number of the worst smells aren’t technical in any respect; they’re organizational. Groups could minor debt 😪 as a result of it doesn’t damage instantly, however the hidden value reveals up later: possession and requirements don’t scale. When the unique authors go away, get promoted, or just transfer on, poorly structured code will get handed to another person 🫩 with out shared conventions for readability, modularity, exams, or documentation. The result’s predictable: upkeep turns into archaeology, supply slows down, threat will increase, and the one that inherits the system typically inherits the blame too.

Checklists: a summarized record of suggestions

This can be a complicated subject that advantages from senior engineering judgment. A guidelines gained’t substitute platform engineering, utility safety, or skilled reviewers, however it can cut back threat by making the fundamentals constant and tougher to skip.

1. The lacking piece: “Downside-first” design

A “design-first / problem-first” mindset signifies that earlier than constructing a knowledge product or AI system (or constantly piling options into prompts or if/else guidelines), you clearly outline the issue, constraints, and failure modes. And this isn’t solely about product design (what you construct and why), but in addition software program design (the way you construct it and the way it evolves). That mixture is difficult to beat.

It’s additionally necessary to keep in mind that know-how groups (AI/ML engineers, knowledge scientists, QA, cybersecurity, and platform professionals) are a part of the enterprise, not a separate entity. Too typically, extremely technical roles are seen as disconnected from broader enterprise issues. This stays a problem for some enterprise leaders, who could view technical specialists as know-it-alls quite than professionals (not all the time true) [2].

2. Code Guardrails: High quality, Safety, and Habits Drift Checks

In follow, technical debt grows when high quality relies on individuals “remembering” requirements. Checklists make expectations express, repeatable, and scalable throughout groups, however automated guardrails go additional: you’ll be able to’t merge code into manufacturing except the fundamentals are true. This ensures a minimal baseline of high quality and safety on each change.

Automated checks assist cease the commonest prototype issues from slipping into manufacturing. Within the AI period, the place code could be generated quicker than it may be reviewed, code guardrails act like a seatbelt by imposing requirements persistently. A sensible approach is to run checks as early as attainable, not solely in CI. For instance, Git hooks, particularly pre-commit hooks, can run validations earlier than code is even dedicated [5]. Then CI pipelines run the complete suite on each pull request, and department safety guidelines can require these checks to go earlier than a merge is allowed, making certain code high quality is enforced even when requirements are skipped.

A stable baseline often contains:

• Linters (e.g., ruff): enforces constant model and catches frequent points (unused imports, undefined names, suspicious patterns).
• Assessments (e.g., pytest): prevents silent conduct adjustments by checking that key capabilities and pipelines nonetheless behave as anticipated after code or config edits.
• Secrets and techniques scanning (e.g., Gitleaks): blocks unintentional commits of tokens, passwords, and API keys (typically hardcoded in prototypes).
• Dependency scanning (e.g., Dependabot / OSV): flags weak packages early, particularly when prototypes pull in libraries rapidly.
• LLM evals (e.g., immediate regression): if prompts and mannequin settings have an effect on conduct, deal with them like code by testing inputs and anticipated outputs to catch drift [6].

That is the brief record, however groups typically add extra guardrails as methods mature, resembling kind checking to catch interface and “None” bugs early, static safety evaluation to flag dangerous patterns, protection and complexity limits to forestall untested code, and integration exams to detect breaking adjustments between providers. Many additionally embrace infrastructure-as-code and container picture scanning to catch insecure cloud setting, plus knowledge high quality and mannequin/LLM monitoring to detect schema and conduct drift, amongst others.

How this helps

AI-generated code typically contains boilerplate, leftovers, and dangerous shortcuts. Guardrails like linters (e.g., Ruff) catch predictable points quick: messy imports, lifeless code, noisy diffs, dangerous exception patterns, and customary Python footguns. Scanning instruments assist forestall unintentional secret leaks and weak dependencies, and exams and evals make conduct adjustments seen by operating check suites and immediate regressions on each pull request earlier than manufacturing. The result’s quicker iteration with fewer manufacturing surprises.

Launch guardrails

Past pull request to manufacturing (PR) checks, groups additionally use a staging atmosphere as a lifecycle guardrail: a production-like setup with managed knowledge to validate conduct, integrations, and price earlier than launch.

3. Human guardrails: shared requirements and explainability

Good engineering practices resembling code evaluations, pair programming, documentation, and shared group requirements cut back the dangers of AI-generated code. A typical failure mode in vibe coding is that the creator can’t clearly clarify what the code does, the way it works, or why it ought to work. Within the AI period, it’s important to articulate intent and worth in plain language and doc choices concisely, quite than counting on verbose AI output. This isn’t about memorizing syntax; it’s about design, good practices, and a shared studying self-discipline, as a result of the one fixed is change.

4. Accountable AI by Design 

Guardrails aren’t solely code model and CI checks. For AI methods, you additionally want guardrails throughout the complete lifecycle, particularly when a prototype turns into an actual product. A sensible strategy is a “Accountable AI by Design” guidelines protecting minimal controls from knowledge preparation to deployment and governance.

At a minimal, it ought to embrace:

• Information preparation: privateness safety, knowledge quality control, bias/equity checks.
• Mannequin improvement: enterprise alignment, explainability, robustness testing.
• Experiment monitoring & versioning: reproducibility by way of dataset, code, and mannequin model management.
• Mannequin analysis: stress testing, subgroup evaluation, uncertainty estimation the place related.
• Deployment & monitoring: monitor drift/latency/reliability individually from enterprise KPIs; outline alerts and retraining guidelines.
• Governance & documentation: audit logs, clear possession, and standardized documentation for approvals, threat evaluation, and traceability.

The one-pager of determine 1 is simply a primary step. Use it as a baseline, then adapt and increase it together with your experience and your group’s context.

The one-pager of determine 1 is simply a primary step. Use it as a baseline, then adapt and increase it together with your experience and your group’s context.

5. Adversarial testing

There may be in depth literature on adversarial inputs. In follow, groups can check robustness by introducing inputs (in LLMs and basic ML) the system by no means encountered throughout improvement (malformed payloads, injection-like patterns, excessive lengths, bizarre encodings, edge circumstances). The hot button is cultural: adversarial testing have to be handled as a standard a part of improvement and utility safety, not a one-off train.

This emphasizes that analysis isn’t a single offline occasion: groups ought to validate fashions by way of staged launch processes and constantly keep analysis datasets, metrics, and subgroup checks to catch failures early and cut back threat earlier than full rollout [8].

Conclusion

A prototype typically seems small: a pocket book, a script, a demo app. However as soon as it touches actual knowledge, actual customers, and actual infrastructure, it turns into a part of a dependency graph, a community of elements the place small adjustments can have a shocking blast radius.

This issues in AI methods as a result of the lifecycle includes many interdependent shifting components, and groups not often have full visibility throughout them, particularly in the event that they don’t plan for it from the start. That lack of visibility makes it tougher to anticipate impacts, notably when third-party knowledge, fashions, or providers are concerned.

What this typically contains:

• Software program dependencies: libraries, containers, construct steps, base photographs, CI runners.
• Runtime dependencies: downstream providers, queues, databases, characteristic shops, mannequin endpoints.
• AI-specific dependencies: knowledge sources, embeddings/vector shops, prompts/templates, mannequin variations, fine-tunes, RAG information bases.
• Safety dependencies: IAM/permissions, secrets and techniques administration, community controls, key administration, and entry insurance policies.
• Governance dependencies: compliance necessities, auditability, and clear possession and approval processes.

For the enterprise, this isn’t all the time apparent. A prototype can look “achieved” as a result of it runs as soon as and produces a consequence, however manufacturing methods behave extra like dwelling issues: they work together with customers, knowledge, distributors, and infrastructure, and so they want steady upkeep to remain dependable and helpful. The complexity of evolving these methods is straightforward to underestimate as a result of a lot of it’s invisible till one thing breaks.

That is the place fast wins could be deceptive. Pace can conceal coupling, lacking guardrails, and operational gaps that solely present up later as incidents, regressions, and dear rework. This text inevitably falls in need of protecting every thing, however the objective is to make that hidden complexity extra seen and to encourage a design-first mindset that scales past the demo.

References

[1] Martin, R. C. (2008). Clean code: A handbook of agile software craftsmanship. Prentice Corridor. 

[2] Hunt, A., & Thomas, D. (1999). The pragmatic programmer: From journeyman to master. Addison-Wesley.

[3] Kanat-Alexander, M. (2012). Code simplicity: The fundamentals of software. O’Reilly Media. 

[4] Anderson, E., Parker, G., & Tan, B. (2025, August 18). The hidden costs of coding with generative AI (Reprint 67110). MIT Sloan Administration Overview.

[5] iosutron. (2023, March 23). Build better code!!. Lost in tech. WordPress. 

[6] Arize AI. (n.d.). The definitive guide to LLM evaluation: A practical guide to building and implementing evaluation strategies for AI applications. Retrieved January 10, 2026, from Arize AI. 

[7] Gomes-Gonçalves, E. (2025, September 15). No Peeking Forward: Time-Conscious Graph Fraud Detection. In the direction of Information Science. Retrieved January 11, 2026, from In the direction of Information Science.

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