Forecasts about what AI agents might become are countless. Yet, one reality is already evident: they are becoming significantly better at automating business processes and supporting specific professional groups, including software engineers.
If we talk in numbers, in 2025:
- 62% of survey respondents say their organizations are at least experimenting with AI agents
- 56% report a measurable decrease in engineering costs thanks to AI usage
Over the past year, AI agents have made a tangible technological leap. Their impact is no longer abstract. Many businesses already feel it in speed, cost, and delivery quality. Railsware is no exception.
As a product studio founded by engineers to build products with impact and transparency, we could not ignore this shift. In this article, our engineers shared five real scenarios in which AI agents delivered significant productivity gains.
Disclaimer: By the time we wrapped this up, AI had already moved forward and our engineers had pushed it further into their workflows. So yes, expect a Part 2 with updated insights.
Unit tests with no excuses
Although unit tests have always mattered, they have also always been postponed. Surely, as writing them takes time, edge cases get easy to miss, and deadlines usually win. Thus, most projects end up with incomplete or nonexistent test coverage.
However, now tests can be generated automatically in seconds. They can be created alongside feature development, following the project’s architecture and covering overlooked edge cases. This way, testing finally becomes a natural part of the workflow, with code quality being improved without adding friction.
I think, we should add that AI also helps with integration tests. In web development, backend coverage is often near 100%, but frontend tests lag behind. Historically, frontend code was harder to test, so many projects carry tech debt (a huge amount of JavaScript code not covered by tests). It is much easier to solve using AI, too.
Easier access to the right information
As engineers, we don’t need multiple tabs, documentation, forum threads and blog posts to find the right solution. All you need is to describe the problem and provide context. You can feed chats with both external context (framework docs, libraries, known pitfalls) and internal context into the model. The best working way is to add external information tailored to the project’s needs. Then, LLM models like Gemini provide you with structured, actionable information within 5-30 minutes rather than a day.
Moreover, you can combine outputs from various sources, such as Gemini, Claude, and ChatGPT, to get the best answer.
AI is great for debugging and understanding context. It spots what’s easy to miss, adds or analyzes logs, and helps pre-plan or onboard new engineers. You can quickly learn data flows or visualize a module.
Debugging support is huge. It’s like pair programming, but on demand. AI becomes your conversational partner, replacing the ‘rubber duck’ from traditional debugging.
Better language mobility
Today, you can be a swiss-army-knife, switching languages or picking up one you haven’t used in years, which used to slow down development dramatically. Syntax, idioms and ecosystem quirks took time to remember or re-learn. However, with AI agents, if you understand the concepts and a similar language, moving between languages is straightforward.
For example, I could build a workable Go project over several evenings using prompt coding, despite not having practised the language for twelve years.
Tools like Cursor’s “Ask” mode make this even easier. You can ask how to approach something unfamiliar, get clear explanations of topics, and see working examples of code lines you don’t fully understand. Learning a new language has become part of development itself, not a separate, slow process.
Quicker planning
The same applies to planning. With AI agents, you can describe a task, generate an implementation plan, refine it, and proceed to execution. While not suitable for every complex scenario, this workflow often works immediately or requires only minor adjustments.
I like to use AI to apply requested changes and address PR comments. I am fortunate to have team members who provide enough information in the comments that the coding agent can handle them. Before it required switching context, changing branch, nowadays it can all happen in the background.
Effortless scripts
Writing scripts for validation, testing, or quick experiments used to take hours. Among all of these efforts, focus would break, context would be lost, and the task always felt bigger than it needed to be.
Yet, at this moment, the process is simple, direct, and effective. The individual time savings might seem small, but over weeks, these tiny wins add up to a meaningful increase in productivity.
For me, it’s not just about time, it’s also about energy and effort. I have always struggled to grind through mundane tasks. Now I can offload some of the hassle to LLM and focus on what matters.
AI agents give teams confidence to tackle what was previously considered too big or too risky, like refactoring the application core, replacing legacy frameworks, etc.
Frankly speaking, I’m adding my input to this article while AI is preparing a script for me:) So it is about multitasking too, you can do many things at the same time.
No turning back
The era of AI agents is here, and its impact on software engineering is undeniable. As our engineers say, skipping tools like Cursor or other AI assistants is like trying to sail without wind.
By 2026, AI agents have proven their value in real-world development workflows. They accelerate routine tasks, automate unit tests, provide context-aware guidance, and allow engineers to focus on high-impact work.
Still, AI is not a universal replacement. Certain tasks remain context-dependent, and human expertise remains irreplaceable. As our co-CEO noted in a previous article on “vibe coding,” AI does not deliver a universal 100% productivity boost. Its real strength lies in augmenting engineers, providing targeted, actionable insights that enhance problem-solving, coding accuracy, and project velocity.