Svoi.ru cuts test preparation time by 70% with AI agents

AI already helps QA not only write automated tests but also eliminate the most expensive hidden stage: preparing to check a product. The Svoi.ru team showed that AI agents can take over requirements analysis, context gathering and drafting of test documentation, cutting the time for this stage by roughly 70%.

The problem the team focused on will be familiar to almost any tester. From the outside, QA work often looks like running scenarios and hunting for defects, but inside the process a significant share of resources goes out long before the first click in the system. You have to read the requirements, pull up related tasks, reconcile documentation versions, work out exactly how the business logic operates, find dependencies between services and rebuild the full picture from scattered sources. When a product evolves quickly, this preparation stage starts eating hours, sometimes days, especially when information lives in several systems and is not updated at the same time.

This is where AI proved useful, not as a universal test generator but as a tool for analytical routine. Instead of trying to replace the tester entirely, the team focused on a narrow, specific area: speeding up the collection and structuring of information before testing. The logic is simple: the sooner a specialist has a coherent picture of a feature, the sooner they can move on to assessing risks, choosing scenarios and actually checking system behavior. This approach removes one of the main bottlenecks in the QA process: constant switching between requirements, tickets, comments, mockups and internal agreements that are rarely gathered in one place.

According to the case description, the AI agents served as a layer between the tester and the sources of knowledge. They help read the input materials, extract key entities, gather context for the task and prepare a clear foundation for further work. In this format, the agent is valuable not because it makes the final decision but because it saves time on searching for and assembling data. The QA specialist is still responsible for quality, priorities and final interpretation, but spends less effort on mechanical actions: copying facts, checking wording and writing the first draft of test documentation.

The 70% effect looks especially significant because it is not a local speedup of a single operation but a reduction in load across the entire preparation loop. When time goes not to reading a dozen documents but to meaningful test coverage, the team reaches complex scenarios faster, finds gaps in requirements earlier and depends less on manual knowledge handoffs between people.

Such agents can also be useful to adjacent roles: analysts, developers and quality managers. If a single mechanism can gather context and make it readable, the benefit goes beyond QA to the whole change delivery cycle.

It is also important that this result does not mean an automatic replacement of testers. On the contrary, the case shows a more mature approach to adopting AI: not substituting expertise but strengthening it where people spend time without adding new value. Test preparation is a good candidate for this kind of automation because it involves many repeatable actions, a lot of text-based information and a high risk of losing details when assembling the overall picture by hand. The more complex the product and the more business rules it has, the greater the benefit from an assistant that quickly consolidates data into a single view.

For the market, this is another signal that the next stage of AI adoption in development is not only about code generation. The most visible returns often come from less public but costly processes: requirements analysis, preparation of artifacts, context handoff and reduction of operational routine. If such practices take hold, the QA role will shift even further toward exploratory and product expertise, while routine preparation will increasingly be handled by specialized AI agents.