AI coding assistants have made it look dangerously easy to believe software can now be built by prompt alone.
In a recent conversation with a few Infobip engineers, we asked whether that promise holds up in practice – and the answer was clear: AI can generate code fast, but it still cannot understand the problem, define the boundaries, or own the consequences.
That part remains the developer’s job.
AI should be a tool for accelerating clearly defined tasks #
Better context and clearer specifications make useful, maintainable, and secure output far more likely. As Zvonimir Petković, Staff Engineer, explained:
The quality of the code ultimately depends on the context given to the GenAI agent and the model underneath. The software engineer is still the one writing the specifications, and the better the specification and context, the better the code produced.
To maintain quality, he says, we need to isolate the code into smaller segments and check each one. Effective work with coding agents is less about one large prompt and more about small, controlled iterations.
That may mean changing the architecture, refactoring a component, or requesting a more precise implementation of a single interface. František Lučivjanský, Senior Principal Engineer, described a similar workflow:
I work with AI in smaller chunks. I give it a small part, review the result, and steer the agent: “This is not correct; do it this way.” I may also define the architecture differently – for example, by asking it to refactor one part first. These slow iterations help me maintain the same quality I would achieve manually.
Working in smaller chunks helps developers preserve a mental model of the system and review decisions while the code is still easy to change. AI serves as a tool for accelerating clearly defined tasks.
AI doesn’t create technical debt, people do #
Faster code generation naturally raises questions about technical debt. Teams have more code to understand, test, and maintain, but AI did not create technical debt. Debt grows from deadlines, trade-offs, and decisions that prioritize short-term delivery over long-term maintainability.
For Tvrtko Ivasić, Application Security Intern, the answer is not to relax established controls, but to reinforce them: We should preserve the standards established in the past: the security pillars, the SDLC pipeline, code review, SAST, and the rest of the process. If anything, the bar should be even higher because the code is now generated by AI rather than written by an engineer.
AI-generated code should go through the same SDLC as human-written code: code review, automated tests, SAST, and dependency checks.
František Lučivjanský notes that agents don’t remove the pressures behind technical debt, but they can help manage it more deliberately. They can also spot duplication, suggest refactors, write tests, or explain legacy code, but the value still depends on the engineer reviewing the output.
Vibe coding might evolve into agent engineering #
Vibe coding may be enough for a hobby project or proof of concept, but problems begin when the same workflow reaches production without additional controls. Engineers may not need to write or memorize every line, but they still need to understand the architecture, system boundaries, scalability, and failure modes, enough to delegate implementation without delegating responsibility.
Asked whether vibe coding is a sustainable approach to software development or merely a short-term productivity boost, Zvonimir argued that it is likely to evolve:
Vibe coding is not just a short-term boost or a passing trend. “Dark factories” may represent the ultimate direction, with workflows that incorporate vibe coding and require us to look at the code less and less. I think it will evolve into agent engineering, and that is how software will be built in the future.
Companies that adopt this workflow may ship faster without sacrificing quality, spending less time on routine code and more on specifications, architecture, evaluation, and automated controls. The key is to understand where vibe coding creates speed, where it introduces risk, and how AI agents fit into proven software engineering principles – because responsibility for what reaches production remains unchanged.
Special thanks to our engineering colleagues from Infobip, the publisher of ShiftMag!