RAG on Steroids: From Semantic Search to an Autonomous Agent and a Digital Twin

Engineers spend up to 30% of their working time hunting for information and for the people who hold it across Jira, Confluence, Git and Slack, and when a key person leaves, the context leaves with them. In this talk I walk through a production AI assistant built on .NET that answers questions over 600,000 fragments of our team's code, commits, tickets and documentation. We start from scratch: what an embedding and cosine similarity are, where pure vector search breaks, and why it needs a second channel in the form of PostgreSQL full-text search. Then comes the hybrid of Qdrant and PostgreSQL: HNSW parameters backed by real recall and latency measurements, chunking, incremental indexing by content hash, result fusion, and source weights tuned by Optuna against a golden set of questions. A separate block covers an MCP server on .NET with 18 tools, role-based search, access control, and defending against prompt injection through a ticket. After that I show an autonomous agent that finds answers on its own through a chain of tool calls, plus a learning loop through Slack that needs no fine-tuning. None of this stays on the slides: the system will answer the audience's questions live on stage, and the finale holds something I am deliberately not announcing in advance. No ML background is required: every concept is explained through familiar .NET analogies, and attendees leave with a set of patterns and a plan for starting small and growing the system into an agent.