AI in app development means using machine learning tools to help plan, write, test, review and improve software, and to add features such as personalisation to the apps themselves. In 2026 AI coding tools no longer just complete lines of code: agentic assistants can plan and carry out multi-step tasks across a codebase, with developers reviewing and directing the work.
AI is not replacing people. It helps teams work faster and do things that were not practical before, but it also brings new risks, from insecure generated code to legal obligations under the EU AI Act. This guide explains what has changed since 2025, what vibe coding is, where AI helps across the development lifecycle, what still needs human judgement and how to check AI-generated code before it reaches production.
TL;DR / Key Takeaways
- AI coding tools have moved from autocomplete to agents that plan, edit files, run commands and open pull requests for review.
- Vibe coding, building software by describing it to an AI and accepting the result, is useful for prototypes but risky for production code.
- AI speeds up planning, coding, testing and debugging, and powers personalisation inside apps, from recommendations to adaptive onboarding.
- AI-generated code can be insecure, poorly structured or based on outdated libraries; it needs the same review and testing as any other code.
- People still own the product decisions, the architecture, the security review and the responsibility for what ships.
What has changed in AI app development since 2025?
Since 2025 AI coding tools have become agentic. Instead of suggesting the next line, agentic tools such as the agents in GitHub Copilot or Claude Code can read a codebase, plan a change, edit several files, run tests and propose the result for review. AI code review and AI-generated tests have also become standard features of development platforms.
What changed since 2025, in short:
- Agentic coding assistants: tools that take a task, work across multiple files and tools, and hand back a change for review. Claude Code, for example, is described as an agentic coding tool that reads your codebase, edits files, runs commands and integrates with development tools.
- AI code review: platforms now offer automated review of pull requests; the GitHub Copilot documentation covers Copilot code review and agentic code review alongside third-party coding agents.
- AI test generation: assistants draft unit and end-to-end tests from code or requirements, and help explain failures.
- Regulation: the EU AI Act entered into force on 1 August 2024 and became applicable on 2 August 2026, with some obligations, such as AI literacy, applying from 2 February 2025.
The result is that writing code is rarely the bottleneck. Reviewing, testing and integrating it, and deciding what to build, take a larger share of the work.
What is vibe coding?
Vibe coding is building software by telling an AI what you want and letting it write the code, often without reading it closely. The term is attributed to Andrej Karpathy, a co-founder of OpenAI, who in a post on 2 February 2025 described fully giving in to the vibes and forgetting that the code exists.
Merriam-Webster defines vibe coding as writing code, making web pages or creating apps by just telling an AI program what you want and letting it create the product for you. It lets people with little programming experience build working prototypes.
That is also its limit. Code nobody has read may work in a demo and still contain security holes, fragile logic or dependencies nobody chose deliberately. Vibe coding is a good way to explore an idea or build a throwaway prototype; for software that handles real users or real data, the code must be reviewed like any other.
How do AI coding tools help developers write code?
AI coding tools help developers by generating code from plain-language instructions, completing functions, translating code between languages and handling repetitive work. A developer can describe a task, for example "sort this list by date", and get working code in seconds, then spend the time saved on design, edge cases and review.
Writing code takes time, and even skilled developers forget syntax or how to implement a particular feature. Tools like GitHub Copilot suggest whole blocks of code in real time, and agentic tools can now implement a complete change across several files. This does not mean developers stop coding; it means less time on boilerplate and more on real problems.
AI also helps across programming languages. A developer comfortable in Python who needs to work in JavaScript can ask an assistant to translate or explain code. The output is not always right, so it must be tested, but it lowers the cost of working in an unfamiliar stack.
How does AI help with planning, testing and debugging?
AI helps at both ends of development. In planning, it analyses user behaviour data to spot trends and turns short text descriptions into wireframes and mockups. In testing, it generates and runs many scenarios, including unusual edge cases. In debugging, it analyses logs and suggests the likely causes of a crash.
App development usually starts with a lot of guesswork about what users want. By analysing behaviour data, AI tools can show which features are likely to be used, and some tools generate a rough visual layout from a few sentences. It is fast pattern recognition, not magic, but it gives the team something concrete to discuss sooner.
Testing is one of the most time-consuming parts of development. AI can simulate what happens when a user of an online shop adds 200 items to the cart or checks out with an expired promo code, and catch problems before real users do. For debugging, it can read thousands of lines of error reports and return a shortlist of likely causes, which saves hours.
How does AI personalise the user experience?
AI personalises apps by learning from how each person uses them. It powers content recommendations, adapts onboarding to the user's experience, tailors product suggestions and offers, and times notifications for when people are most likely to respond. In a crowded app market, users increasingly expect an app to feel made for them.
The most familiar example is recommendations: Spotify suggesting music or YouTube suggesting videos by comparing what you liked with what similar users like. Adaptive onboarding goes further, giving a beginner a full tutorial while an experienced user skips to advanced features; language apps such as Duolingo and Babbel adjust lesson difficulty in a similar way.
In e-commerce apps, AI analyses browsing behaviour, purchase history and time spent on product pages to suggest products and time-sensitive offers. To do this, apps may track which features you use, how long you stay on each screen, your preferred themes and even your location.
That raises real privacy questions. Responsible developers build in clear consent options, collect only the data they need, and anonymise or encrypt it where possible. Personalisation should make the app better for users, not make them uncomfortable.
What are the risks of AI-generated code?
The main risks of AI-generated code are security flaws, poor maintainability and hidden dependencies. Generated code may skip input validation, use outdated or vulnerable libraries, miss edge cases or work correctly while being badly structured. Because it looks plausible, these problems are easy to miss unless every change is reviewed and tested.
AI also needs good data. A recommendation model trained on one region's users may perform poorly elsewhere, and a system trained on biased data will make biased decisions. It lacks context, too: it does not know why a user is frustrated or which trade-offs matter to your business.
There is a team risk as well. If developers let AI do all the thinking, they gradually lose the hands-on skills needed to judge its output. For codebases already built with heavy AI assistance, an independent software audit is a practical way to find security issues, fragile areas and technical debt before they become incidents.
When the app itself contains AI features, legal obligations apply too. Our guide to EU AI Act deployer obligations and deadlines explains what companies that use AI systems need to do in 2026.
How do you check AI-generated code before it ships?
Treat AI-generated code exactly like code written by a new team member: review every change, run it through automated tests and security checks, and make sure someone on the team understands it well enough to maintain it. The pipeline should not know or care who wrote the code, only whether it passes.
- Review: every change is read by a person who can explain what it does and why.
- Test: unit, integration and end-to-end tests cover the change; generated tests are checked to make sure they would fail if the code were wrong.
- Scan: dependency, secret and static-analysis scans run on every change.
- Own: a named developer is responsible for each area of the codebase, however it was written.
- Record: note where AI tools were used on significant changes, so reviewers know where to look harder.
For companies adding AI to existing systems rather than writing new apps, our guide to integrating AI into enterprise software covers architecture and data. To judge whether the investment pays off, see how CTOs measure AI returns.
How is AI changing the way development teams work?
AI supports the whole team, not just developers. It helps prioritise bug reports, suggests release timing from past performance, drafts documentation, generates layout ideas for designers and helps QA teams find inconsistencies across devices. The overall effect is that small teams can build what once needed many more people.
The benefits are real: time saved on repetitive work, earlier detection of bad practices and vulnerabilities, more personalised apps, decisions based on data rather than guesses, better team productivity and a lower barrier to entry for people starting out. Each of them depends on people using the output critically.
Success still depends on people: their ideas, instincts and judgement. AI helps turn those into working software faster, but deciding what to build, how it should be designed and whether it is safe to release remains a human responsibility.
If you are unsure what AI tools have left in your codebase, write to us at business@wwg.it.
Sources
- Merriam-Webster, Vibe coding — https://www.merriam-webster.com/slang/vibe-coding
- GitHub, GitHub Copilot documentation — https://docs.github.com/en/copilot
- Anthropic, Claude Code overview — https://code.claude.com/docs/en/overview
- European Commission, AI Act: regulatory framework on AI — https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai
FAQ
Frequently Asked Questions
No. AI coding tools take over much of the repetitive work, but someone still has to decide what to build, design the architecture, review the generated code, handle security and take responsibility for what ships. The developer's job is shifting from typing code towards directing, reviewing and integrating it, which makes experience and judgement more valuable.
Not on its own. Vibe coding is fine for prototypes and experiments, but production apps that handle real users or data need code that someone has read, tested and can maintain. If a product started as a vibe-coded prototype, it should be reviewed and partly rebuilt before launch, especially around authentication, data handling and payments.
Developers commonly use AI assistants built into their editor or development platform, such as GitHub Copilot, and agentic tools such as Claude Code or OpenAI Codex that can work across a whole codebase. Most platforms also offer AI code review and test generation. The right choice depends on your stack, security requirements and where your code is hosted.
You check it the same way as any other code: human review, automated tests, and dependency, secret and static-analysis scans on every change. Pay particular attention to input validation, authentication, access control and third-party libraries. For an existing codebase built quickly with AI, an independent security review or software audit gives a reliable baseline.
