[new @arxiv paper] There’s a huge gap between playing with AI skills in a demo and ⚙️ engineering them for production. In a simple setup, semantic routing feels like enough: the user asks something, the LLM matches the text to a skill description, and executes it. But in real-world production at scale (like @Wix Customer Care), semantic matching alone isn't enough. 🚩 Why? Because semantic matching only looks at what the user says, completely ignoring what the user is actually allowed or able to do. A user asking to "connect a domain" is a 100% semantic match for the Domain Skill. But if they don't have an active premium plan, executing that skill is a dead end. In a new arXiv paper by @AshkenaziOrtal, Vitalii Kloz, and Mykhailo Ulianchenko, we present Helpmate’s (Wix’s Customer Care AI agent) architecture: introducing a deterministic executability gate that evaluates account state before candidate skills reach the LLM. Tested across 756K+ user messages in production: 🎯 59.4% of semantically matched candidates were filtered out because the account state made them non-executable 📉 90.5% reduction in skill context size 228.8M tokens saved 🛡️ In a targeted replay of 1,000 risky conversations, removing the gate caused the model to select a non-executable skill in 7.8% of cases. Read the full paper: arxiv.org/html/2608.01050v1