Agentic Feature Engineering: How McAfee Drives Personalization With Agent Bricks

McAfee protects millions of users through products like Scam Detector and Social Privacy Manager. Iteration on adaptive pricing models was bottlenecked by slow, manual feature engineering. To unlock value from massive user signals, we needed to move from ideation to production fast. In this talk, we show how we built an autonomous feature engineering agent on Mosaic AI that cut cycles by 60% and drove $54M+ in revenue — replacing manual SQL with an agent that reasons, iterates, and converges. Attendees will see a live blueprint of our agentic pipeline: Analyze & Ideate — Agent loads domain context and playbook from Unity Catalog, analyzes baseline, and proposes features via LLM reasoning. Generate & Evaluate — Agent writes code, executes in sandbox, evaluates against baseline, logs every iteration to MLflow. Converge & Deploy — Best features publish directly to Unity Catalog Feature Store. Walk away with a reproducible blueprint for agentic feature engineering on Databricks.