Everstone AI Blog

There’s a metric that kills AI products faster than hallucinations or bad UI: the Spinning Wheel of Death . When a user asks a chatbot a question, they start a mental timer: 0.1 seconds : instant 1.0 second : it’s thinking 10.0 seconds : it’s broken (and they leave) Those numbers aren’t random. They’re classic UX principles. But in enterprise AI—where agents query databases, search documents, and check policies— 10-second responses are common . If you present that as a 10-second loading spinner, your product will lose users. The good news: people are surprisingly patient when they can see progress. That’s the Latency Illusion . You don’t always need the agent to finish faster. You need it to start responding sooner . Here’s how Product Managers and Engineers can work together to build responsive AI agents on Databricks.

There’s an old rule in engineering known as the Iron Triangle: Fast, Good, Cheap — pick two . In Generative AI, many organizations assume it’s even harsher: you only get to pick one. If you want Fast , you pay for bigger GPU capacity (not cheap). If you want Cheap , you accept slower shared endpoints or smaller models (not fast). That assumption creates the High-Performance Paradox. To reduce latency, teams buy more capacity—switching to larger Provisioned Throughput endpoints and doubling the monthly bill just to shave 500ms off response time. It’s not sustainable. Here’s the practical truth: most GenAI latency isn’t the model. It’s everything around it—database queries, document retrieval, tool calls, and retries. The way out isn’t brute force. It’s concurrency . By changing how your agent handles waiting—specifically through asynchronous I/O and streaming —you can make the experience feel dramatically faster, while improving throughput and lowering unit costs. Here’s how to optimiz...