2 sources filed · EN 1 · RU 1
Step 1: existing code search across about 1,000 repos wrapped as an MCP tool, then docs and wiki readers, feature flags, task management and the data platform.
30exposed tools beyond which agents slow down and degrade, per the speaker
February 2025: in-house tools and fine-tuning on Avito's code base; adoption was the key metric and "went quite badly".
-15%2026 cycle-time target, already beaten by July
2 sources filed · RU 2
Request and token counts were dropped as adoption metrics because agents and autocomplete inflate them.
50% vs 3%share of people using any LLM chat monthly versus share using the API (agents, autocomplete) after awareness campaigns
Faros AI 2025 ("The AI Productivity Paradox", 1,255 teams): heavy AI users closed 21% more tasks and merged 98% more PRs, while review time rose 91%, PR size 154% and bugs per developer 9%; no significant company-level effect.
+300.6%rise in time spent in QA, the largest flow-metric deterioration in Faros AI's "The Speed Trap" (September 2026)