AlpinaGPT Implemented Multi-Agent System Evolver and Cut Code Review to 15 Minutes

CTO of AlpinaGPT and founder of outsourcing company WebRegul Sergey Andrianov at the end of May 2026 at an internal master meeting of Alpina Digital on AI transformation spoke about how the multi-agent system Evolver reduced code review time from two days to several hours, and on routine merge requests — to fifteen minutes, freeing up about a hundred hours of work per month for the team.

How code review became hell

According to Andrianov, WebRegul — an outsourcing development company that has been working on the market for more than fifteen years — two years ago faced rapid growth: the number of developers tripled in one year, and processes failed to adapt to the new scale of the team.

"Code review from a useful practice in places began to turn into hell,"

Sergey Andrianov said.

Review wait times stretched to two days, which slowed down releases and reduced developer motivation to wait for feedback on their code.

• Growth of developer staff — tripled in one year
• Code review wait time before Evolver implementation — up to two days
• After implementation — a few hours, on routine MR — 15 minutes
• Team time savings — about 100 hours of work per month

What the Evolver system does

Evolver is a multi-agent system that Andrianov's team built to automate code review. Several AI agents take on the initial analysis of merge requests: they find typical errors, check compliance with code standards, and prepare structured review before the engineer is engaged in the process.

Why no autonomous agents in production

The key decision of the team — a conscious rejection of fully autonomous agents that make decisions about code quality on their own and merge changes without human control. Evolver is integrated into the process as an assistant: agents accelerate and structure the routine, but the final decision on the merge request always remains with the human engineer. This is a choice in favor of predictability and quality control, rather than maximum automation at any cost.

The case was shared at the Alpina Digital meeting by Zhemal Hamidun, CPO of AlpinaGPT and Head of AI at Alpina Digital — according to his observation, such implementations of multi-agent systems in routine engineering processes remain rare practice on the market, rather than a common occurrence.

What this means

The Evolver case shows that multi-agent systems are already delivering measurable results not in futuristic scenarios, but in routine engineering work — where the cost of error is high and the amount of routine is large. At the same time, successful implementations choose to keep the human in the decision loop, rather than full agent autonomy, which reduces risks and makes it easier for the team to trust the new tool.

Frequently Asked Questions

How much time does Evolver save?

Code review waiting time dropped from two days to several hours, and on typical merge requests — to fifteen minutes; the team frees up about one hundred hours of work per month.

Why did

WebRegul and AlpinaGPT decide against autonomous agents in production?

The team consciously kept the final decision on code review with the human engineer, using the multi-agent Evolver system as an assistant that speeds up the analysis of merge requests, rather than as an independent judge of code.