Anna Goncharova

Engineering teams struggle to balance documentation quality with development velocity. Manual documentation processes create bottlenecks, while outdated content leads to knowledge gaps and increased support burden. This presentation explores a practical approach: developing an AI agent that integrates directly into existing engineering workflows.

The speaker shares firsthand experience building and deploying an AI agent that operates within the software development lifecycle. The presentation covers the complete development journey from concept to production-ready system, demonstrating how an AI agent connects with version control, responds to code changes, and produces consistent output without disrupting established team processes.

Attendees learn the technical foundations of agent development including API integration with large language models, event-driven architecture design, and CI/CD pipeline automation using GitHub Actions. The talk addresses critical implementation challenges such as authentication handling, error recovery, output quality control, and managing edge cases.

The presentation emphasizes practical integration patterns that allow a documentation AI agent to complement rather than complicate engineering workflows. The speaker discusses how to identify appropriate automation opportunities, set realistic expectations, and measure agent effectiveness.

Participants gain actionable knowledge for evaluating, building, and deploying AI agents within their own engineering organizations.