The Agentic Interview: How AI Learns to Know You

As context windows expand to millions of tokens in 2026, the industry is facing a new crisis: the signal-to-noise ratio in AI memory. Simply dumping data into a model is no longer enough; we need systems that proactively understand us. This episode explores the concept of "agentic interviews"—a shift from passive retrieval-augmented generation to active context extraction where the AI takes the lead. We discuss the technical limitations of "lost in the middle" retrieval, the computational costs of massive windows, and the necessity of "belief revision" to handle the fluid nature of human information. By moving from unstructured chat logs to structured knowledge graphs, AI can finally bridge the gap from a reactive tool to a high-fidelity partner. Learn how a proactive approach to context can transform how we work with agents, ensuring they spend less time sifting through old data and more time being useful from day one.