Marion Schleifer

Hot take: AI Assistants are failing us Despite the buzz, closed-domain AI assistants are falling short. Without reliable, context-aware responses, they’re not ready for serious business use. Where AI Assistants Fail Here’s a scenario from a well known sales assistant that’s out there today: 📊 User Query: “What’s the length of my average sales cycle?” ➡️ Assistant Response: “I calculated the average sales cycle length for your opportunities, but there are no results to show.” The assistant can’t perform a computation. Why? Let’s break it down. 🛑 The Issue: Closed-domain AI assistants rely heavily on search-first RAG methods, making them unsuitable for high-trust applications. Consider a task like “Find all emails from last week that need follow-ups.” A search-based AI might skip important messages if they lack specific keywords, leaving critical follow-ups unnoticed. When this incomplete data is passed to the language model, the result is unreliable, making these assistants ill-suited for nuanced business queries. ✅ The Solution: Agentic query planning. Instead of rigid keyword search, assistants should gather all relevant emails and then use an LLM to classify follow-ups—just as a person would—ensuring accuracy. That’s why our AI lab built PromptQL - an agentic, data access API for your AI! Here’s a look at what we’ve been up to ⤵️

Subtle evolution in how I'm thinking about Grats: Grats treats GraphQL SDL as a compiler target, and emits a description of your TypeScript server's capabilities rather than asking you to define your schema in your Typescript code 1/2