How a product team halved engineer onboarding with AI agents

The team maintained 14 microservices, 200+ API endpoints, and a monorepo with five years of git history. Documentation existed in Notion, Confluence, Google Docs, and scattered README files. New engineers spent their first six weeks doing 'knowledge archaeology' — asking Slack questions and reading stale wikis.

Sprint retrospectives repeatedly surfaced the same complaint: 'I didn't know that service existed' or 'I couldn't find the ADR for that decision.' Institutional knowledge was concentrated in two senior engineers who had been there since day one.

The team had experimented with AI coding assistants, but without a curated knowledge base, the assistants hallucinated outdated patterns and ignored internal conventions. The output required more review time than it saved.

RuNari's Knowledge Base indexed the team's entire documentation footprint: Notion pages, Confluence spaces, README files, and 50+ Architecture Decision Records. Semantic search meant engineers could ask natural-language questions ('How does the payment service handle refunds?') and get accurate answers in milliseconds.

AI Agents were configured as specialist assistants — one for backend patterns, one for frontend conventions, one for infrastructure. Each agent drew context exclusively from the verified knowledge base, eliminating hallucination. Work orders automated routine tasks like generating boilerplate, updating API docs, and running regression suites.

Project Management replaced Jira for sprint planning. Tasks linked directly to knowledge base documents, so every ticket carried its full context. The AI planner agent automatically estimated complexity based on similar past tickets.