Voice Agents are here. Context turns them into real customer value.
Voice agents only create real customer value when they have context on who is calling, so they can adapt to that person, stay within set rules, and improve with every call. On Databricks, the agent's reasoning sits next to your governed data, with context and ontology grounding each answer and a learning loop fed by every call, and the DIVA blueprint offers a starting point after you measure one high-volume routine call.
*Voice is already changing how customers interact with brands. People who moved from storefronts to websites to apps now expect to simply talk to the companies they buy from, and the same shift is reaching suppliers, drivers, and field teams. *Most enterprise AI so far has faced inward. The larger opportunity is in the conversations companies have with customers and partners every day, and each of those conversations can now be captured, understood, and acted on in real time. *Any team can build a voice agent that talks back, but value is created only when the agent has context on who is calling. From there, it can adapt to that person, stay within set rules, and get better with every call. *On Databricks, the agent's brain sits next to your governed data. Context and ontology ground every answer, fast AI decisions tailor each turn, and every call feeds a learning loop. *Getting started is straightforward. Pick one high-volume routine call, measure how well the agent handles it, and use the DIVA blueprint as the starting point for the next one.