Why most AI agent frameworks break in production (and what I’m doing differently)

Over the past months I’ve been building a WhatsApp-first AI receptionist that handles booking and rescheduling directly into Google Calendar. One thing became obvious quickly: Most agent frameworks work well locally, but start breaking once you try to run them as a multi-tenant production service. Session state disappears. Memory becomes fragile. PII handling is unclear. Human escalation is missing. So I started building an opinionated framework called Glaivio, applying a convention-over-configuration approach similar to what Rails did for web apps — but for AI agents. The goal isn’t flexibility. The goal is predictable production behavior. Repo: https://github.com/tavyy/glaivio-ai The problem with most agent architectures today Typical agent demos assume: stateless execution single-user workflows local memory files no escalation path no privacy middleware That works for prototypes. It doesn’t work when: multiple customers interact simultaneously conversations persist across sessions m