Consider a three-person start-up that builds a booking platform for physiotherapy clinics. Using an AI coding assistant, the founders produce a working version in under a month: patient sign-up, appointment slot’s, card payments, reminder emails and an admin dashboard. The investor demo goes without a hitch.
Three months after launch, the list looks different. A clinic reports two patients booked into the same slot. Reminder emails stopped for a weekend after the email provider changed an API parameter. A customer asks where API keys are stored and who can read patient notes. The founders want to add group classes, but nobody is sure what will break. And the developer who wrote most of the prompts has left.
The scenario is illustrative, not a real company, but it describes a pattern that many teams adopting AI-assisted development are now meeting. The tools made the application faster to create. They did not make it cheaper to operate, and in some respects they moved work from the start of the project to the months after launch.
Writing code that works in a demonstration and running software that people depend on are different jobs. The second means understanding, testing, securing, monitoring, fixing and supporting the application. AI tools can help with parts of that work, but they do not remove the need for someone to own it.
Why AI-assisted development can create a maintenance blind spot
AI-assisted development covers a wide spectrum. At one end, an experienced engineer uses an assistant to draft a function, then reads, tests and adjusts it before committing. At the other, a non-technical founder describes an application in plain language to an app builder and deploys whatever comes back. The practice sometimes called “vibe coding” sits towards the second end: accepting generated code largely on the basis that it appears to work.
The maintenance risk grows as you move along that spectrum, for a simple reason. Code that nobody on the team has read is code that nobody on the team understands. When it breaks, someone has to work out why, and the time saved by writing it can be repaid with interest during debugging.
Developers report this tension themselves. In Stack Overflow’s 2025 Developer Survey of more than 49,000 respondents, 84% used or planned to use AI tools, yet 46% did not trust the accuracy of their output. Some 66% cited AI solutions that are “almost right, but not quite” as a frustration, and 45% said debugging AI-generated code was more time-consuming.
That does not make AI-generated code inherently worse. A 2026 study presented at the EASE conference compared 508 agent-generated and 508 human-written files across 100 open-source repositories, and found the AI-generated files needed fewer and smaller changes over six months. But humans made 83% of the commits that did modify them, and the authors cautioned that low activity might reflect limited use or difficulty understanding the code rather than higher quality, When generated code needs changing, a person usually has to do it.
Whether that person can depends on a few habits: reviewing generated code as carefully as a new colleague’s pull request; consolidating verbose or duplicated code before more features are built on it; questioning every package a generator pulls in; treating authentication, permissions and sensitive data as the areas where “it works” and “it is correct” diverge most sharply; and designing for timeouts, partial failures and malformed input rather than only the happy path.
The difference between a working demo and a reliable application
A demonstration answers the question “does it work?” with one user, clean data and a stable network. Production asks a harder set of questions, and most of them are invisible until something goes wrong.
The double booking in the opening scenario is a good example. Two patients clicking “confirm” within the same second is unlikely in a demo and routine in production. Preventing it needs a database constraint or a transaction, not just a check in the user interface. A generated booking flow may include the check and omit the constraint, and both versions will pass a manual test.
Integrations fail in similar ways. Third-party APIs change, rate-limit and time out; an application that assumes every call succeeds will eventually fail silently or half-complete a transaction. Retries, timeouts and clear error states make the difference.
Other gaps tend to appear on the same list:
- Edge cases in data: names with apostrophes, time zones, daylight-saving changes, leap years, empty fields.
- Performance under realistic volume, particularly database queries that are fast with 50 rows and slow with 500,000.
- Access control that is tested from the point of view of an attacker or a curious user, not just a legitimate one.
- Logging and monitoring good enough to tell the team something is wrong before a customer does.
- Deployment and rollback, so that a faulty release can be reversed in minutes rather than repaired live.
The gap is not unique to AI-built software. TechBuzzIreland readers who follow gaming will recognize the same pattern from online games that run smoothly in testing but stutter at launch. What AI tools change is how quickly a team can reach the demo stage, and therefore how easy it is to mistake that stage for the finish line.
Technical debt can accumulate faster than teams expect
Technical debt is the future cost of choices made to move quickly now. Some of it is deliberate and sensible: hard coding a configuration to hit a launch date, knowing it will be replaced next quarter. The problem is unmanaged debt, where nobody recorded the shortcut, nobody owns its repayment and the interest compounds with every new feature.
AI-assisted development can raise the rate at which debt is taken on, because code is cheap to produce and easy to accept unread. The interest shows up when requirements change and duplicated logic must be reworked in five places; when two developers’ assistants produce incompatible approaches to related problems; when a dependency upgrade touches code nobody understands; or when a bug is patched three times because each fix treated a symptom.
The hardest case is when the original author leaves. With conventional code, knowledge lives partly in the code and partly in the developer’s head. With heavily generated code, it may live mostly in a prompt history that nobody kept.
Google’s 2024 DORA research found that increased AI adoption was accompanied by an estimated 1.5% fall in delivery throughput and a 7.2% fall in delivery stability Google Cloud. That is a survey-based association, not proof of cause, but it suggests faster code production without matching investment in review and testing does not automatically yield more reliable software.
Security and reliability need attention before and after launch
Most security risks in AI-built applications are ordinary software risks. The led by broken access control and security misconfiguration, and adds a new category, mishandling of exceptional conditions, for poor error handling and systems that “fail open”. None of this is specific to AI; all of it is easy to introduce when code is accepted because it runs.
Generated code does need particular scrutiny, though. Veracode’s 2025 GenAI Code Security Report tested more than 100 models on 80 coding tasks and found security vulnerabilities in 45% of cases, with no improvement as models got better at writing code that works. That is a vendor’s benchmark, not a measure of every application, but it argues against skipping review.
Human behavior matters too. In a Stanford-led user study published at ACM CCS 2023, participants with access to an AI assistant wrote significantly less secure code than those without, and were more likely to believe their code was secure. Confidence and correctness can move in opposite directions.
One risk is genuinely new: models sometimes recommend packages that do not exist. A USENIX Security 2025 study of 576,000 code samples from 16 models found that at least 5.2% of packages suggested by commercial models, and 21.7% by open-source models, were hallucinated on average. An attacker who registers one of those names can get malicious code installed by anyone who trusts the suggestion.
The practical controls are familiar and should run continuously, not just before launch:
- Secrets management: keep keys out of source code and front-end bundles, inject them at runtime, rotate them and scan for them before commit, as OWASP’s recommends.
- Authentication and authorisation enforced on the server for every request, with tests that try to read another user’s data.
- Input validation at every trust boundary, including data from third-party APIs.
- Dependency checks that confirm packages exist, are maintained and have no known vulnerabilities.
- Data protection proportionate to what is stored, including GDPR obligations for Irish and EU businesses.
- Logging, incident response and behavioral monitoring, including watching AI features for changed output after model or prompt updates.
What a sound AI-assisted development process includes
That process starts with requirements and acceptance criteria specific enough to test: not “users can book appointments” but “two users cannot hold the same slot, and a failed payment releases it within five minutes”. It continues with architecture and integration decisions made by a person who understands the trade-offs, because a generator will happily produce three different data-access patterns if asked three different questions.
Human review of generated code is the most important control. Automated tests stop future changes, by a person or an assistant, from silently breaking behaviour; exploratory testing catches what those tests were never written to find; and security checks, including dependency and secret scanning, belong in the build pipeline rather than a pre-launch audit.
Then comes the work that makes maintenance possible: documentation and knowledge transfer covering design decisions and known limitations; deployment and rollback procedures that have been rehearsed; and monitoring and maintenance planning that names who will apply updates, renew certificates and respond to alerts.
Long-term support should shape design from the start: a team that expects to diagnose problems at 2am logs differently and chooses simpler components. Organisations without that experience in-house sometimes bring in AI application development services that combine AI-assisted delivery with engineering review, testing and deployment practice. Either way, the test is whether the people who inherit the application will be able to understand, change and recover it.
Why post-launch technical support matters
In small teams, support is often whoever sees the email first. That stops working when developers move on to new features, users multiply or problems arrive at the weekend. From then on, how support is organized shapes how reliable the product feels.
Good support triages by impact, so a payment failure affecting every customer is not queued behind a password reset. It troubleshoots with documented steps, escalates to engineering with enough detail to reproduce the problem, maintains a knowledge base, and notices recurring incidents: the third ticket this week about missing reminders is one problem, not three.
One of support’s most valuable skills is telling user errors from software defect. “The calendar shows the wrong time” may be a user in a different time zone, a browser setting or a genuine bug in daylight-saving handling. Getting that distinction right protects engineering time and stops real defects being dismissed.
AI tools now help here too: classifying and routing tickets, retrieving relevant articles, suggesting troubleshooting steps, automating repetitive tasks such as account unlocks and reviewing far more interactions for quality than a human team could. Ai saas technical support services typically pair these capabilities with human specialists who handle complex cases and provide oversight.
The limits matter as much. Automated triage can misclassify unfamiliar issues, often the very ones engineering most needs to see. Retrieval can surface an article made obsolete by the last release, and a generated reply can be confident yet incomplete. Data exposures, billing disputes and upset customers need human judgement. Well-run operations set thresholds for automation, keep a person easy to reach and audit a sample of automated resolutions. Published automated-resolution rates, vendors’ included, depend on the product and on how “resolved” is defined.
The missing connection between support teams and developers
When support and engineering work in isolation, support resolves symptoms without learning causes and engineering sees single escalations rather than patterns. With AI-built applications, where understanding of the code may already be thin, that gap is especially costly.
A hypothetical example shows how the connection should work. A clinic manager reports that the booking platform’s AI-generated appointment summary listed the wrong treatment for a patient.
- Support records the circumstances: which clinic, which appointment, when, what the summary said and what it should have said, with screenshots.
- Support checks for recurrence and finds four similar tickets over a fortnight, all involving appointments that were rescheduled.
- Support collects diagnostics (request IDs and timestamps from the logs) and escalates with the pattern, not just the single complaint.
- Engineering investigates and finds that rescheduling creates a new appointment record but the summary feature reads treatment details from the original, now stale, record.
- Engineering tests a correction, adds an automated test for rescheduled appointments, and deploys it behind a feature flag.
- Support updates its documentation, informs the affected clinics and watches for the issue returning over the following weeks.
Each cycle leaves behind a regression test, a knowledge-base entry and a little more shared understanding of a codebase nobody wrote by hand.
Useful measures include the repeat incident rate, mean time to resolution, availability, error rates, the escalation rate to engineering and customer-reported recurring problems. None proves quality on its own. Fewer tickets can mean fewer problems or fewer people bothering to report them; faster resolution can mean better fixes or more workarounds. Read together and over time, they show whether the loop is working.
Build everything internally or bring in specialist support?
There is no universally right answer, and claims that outsourcing is always cheaper, or that in-house is always better, do not survive contact with real budgets.
Keeping development and support in-house maximises control and keeps knowledge close to the product. It suits teams with experienced engineers, a manageable support volume and a product whose domain is hard to explain to outsiders. The risks are thin cover, key-person dependency and support work crowding out development.
Using external development specialists can add skills a team lacks, such as security review, testing discipline or experience taking applications into production. It works best for defined pieces of work with clear acceptance criteria. The risk is knowledge leaving with the contractor unless handover is part of the contract.
Using external technical support can provide extended hours, established processes and capacity that scales with ticket volume. It suits products with a stable feature set and good documentation. Its weakness is distance from engineering, which the feedback loop described above is designed to close.
Combining internal product ownership with external expertise is common in practice: the business keeps ownership of the roadmap, architecture decisions and customer relationships, and engages specialists for particular development or support functions.
The deciding factors are product complexity, existing internal knowledge, the service hours customers expect, security and data-protection obligations, budget and growth plans. Whatever the mix, accountability for the application should sit with a named person inside the business.
A practical checklist for maintaining an AI-assisted application
- Code ownership and documentation: every module has a named owner; design decisions, known limitations and generated sections are recorded.
- Dependency management: packages are pinned, verified as genuine, scanned and updated on a schedule.
- Testing and regression checks: tests cover core workflows and every fixed bug, and run on every change.
- Security review: access control, secrets and input validation are reviewed quarterly and after major releases.
- Monitoring and alerting: every alert has a named recipient.
- Incident escalation: support knows what to escalate immediately and what evidence to include.
- Support knowledge base: articles are updated with each release.
- Engineering feedback loops: recurring tickets are reviewed with engineering and fixed issues become tests.
- Maintenance responsibilities: someone owns certificates, API deprecations, backups and updates.
- Recovery and rollback: both have been rehearsed, with target times.
- Operational review: a monthly look at incident and escalation trends decides what to fix before what to build.
Plan for the life of the application, not just its creation
AI-assisted development has changed the economics of building software. A small team can now produce in weeks what once took months, and that is a real gain. But reliable products are still made from engineering judgement, testing, maintenance, monitoring and support, and those costs have not fallen at the same rate.
The practical lesson is to ask a different question at the start. Not only “how quickly can we build this?” but “who will maintain it, diagnose it when it fails, protect it and improve it once people depend on it?” Some organizations will answer with their own teams; others will work with external specialists such as Flairstech for development or support. Either way, the answer is best decided before launch, while it is still cheap to design for.