Zillow faced a challenge with customer journeys spanning multiple stages and professionals, requiring context to persist across interactions. A single chatbot was insufficient for this complex, extended process. Zillow's SVP of Engineering, Toby Roberts, and Glean's CEO, Arvind Jain, discussed their AI architecture designed to maintain this context. They highlighted that context, not raw data, proved to be the more difficult problem to solve. Zillow's AI efforts began with establishing a strong data foundation using a data mesh and robust governance. However, the real hurdle was creating a system that remembered a customer's progress and carried that information forward across different platforms.Zillow opted to build its own persistent context layer rather than relying on external chat interfaces, recognizing the nature of real estate transactions. Their approach utilizes smaller, task-specific AI models fine-tuned for different purposes, rather than a single, broad model. Internally, Zillow employs thousands of Glean agents to automate repetitive tasks. Glean's platform centralizes integration work, preventing duplication across departments and acting as a cost-saving measure. This is achieved through model routing to less expensive models and precomputed context, significantly reducing token consumption.For enterprises embarking on agentic AI, Zillow and Glean offer key insights. Establishing measurement baselines before AI implementation is crucial for quantifying impact. Centralizing context management avoids redundant integration efforts across teams. Sensitive data requires additional compliance checks beyond automated permissions. Finally, context should be viewed as a cost optimization tool, not just a functional capability, as exemplified by model routing and precomputed context.