AlpinaGPT deploys Evolver multi-agent system, cuts code review to 15 minutes

Sergey Andriyanov, CTO of AlpinaGPT and founder of outsourcing company WebRegul, spoke at an internal Alpina Digital master session on AI transformation in late May 2026. He explained how the Evolver multi-agent system cut code review wait times from two days to a few hours, and to fifteen minutes on routine merge requests. The change freed up about a hundred hours of team time a month.

How code review turned into hell

According to Andriyanov, WebRegul, an outsourced development shop that has been in business for more than fifteen years, hit a growth spurt two years ago. Its developer headcount tripled in a single year, and its processes couldn't keep up with the team's new scale.

"In places, code review went from a useful practice to something close to hell,"

Sergey Andriyanov said.

Review wait times stretched to two days. That slowed down releases and sapped developers' motivation to wait for feedback on their code.

• Developer headcount growth — tripled in one year
• Code review wait time before Evolver — up to two days
• After rollout — a few hours, 15 minutes on routine MRs
• Team savings — about 100 hours of work a month

What Evolver does

Evolver is a multi-agent system that Andriyanov's team built to automate code review. Several AI agents handle the first pass on merge requests: they catch common mistakes, check compliance with coding standards, and prepare a structured review before an engineer ever gets involved.

Why no autonomous agents in production

The team's key decision was to deliberately reject fully autonomous agents that judge code quality on their own and merge changes without human oversight. Evolver sits in the workflow as an assistant: the agents speed up and structure the routine work, but the final decision on a merge request always stays with a human engineer. The team chose predictability and quality control over maximum automation at any cost.

Zhemal Khamidun, CPO of AlpinaGPT and Head of AI at Alpina Digital, presented the case at the Alpina Digital session. In his view, deployments like this, which bring multi-agent systems into routine engineering processes, remain rare in the market rather than commonplace.

What it means

The Evolver case shows that multi-agent systems already deliver measurable results, not in futuristic scenarios but in routine engineering work, where mistakes are costly and the volume of routine tasks is high. At the same time, successful deployments keep a human in the decision loop instead of granting agents full autonomy. That lowers risk and makes it easier for the team to trust the new tool.

FAQ

How much time does Evolver save?

Code review wait times dropped from two days to a few hours, and to fifteen minutes on typical merge requests. The team frees up about a hundred hours of work a month.

Why did

WebRegul and AlpinaGPT reject autonomous agents in production?

The team deliberately left the final code review decision with a human engineer. It uses the Evolver multi-agent system as an assistant that speeds up merge request triage, not as an independent judge of code.