Building Conversational AI Agents That Remember: LangGraph, Postgres Checkpointing, and the Future of Financial UX

How interrupt/resume graph topology turns stateless LLMs into stateful financial advisors — and why this changes everything for CFO-facing AI products. The Problem Nobody Talks About Every demo of a financial AI agent looks the same: the user asks a question, the agent answers, end of story. One shot. One turn. The agent forgets you exist the moment the response is sent. But real financial conversations don't work that way. A CFO doesn't ask a single question and walk away. She starts with "What drove the variance in OPEX this quarter?", gets an answer, then drills down: "Break that out by department." Then pivots: "OK, run a scenario where we delay the European expansion by one quarter - what happens to our cash runway?" Each question builds on the last. Context accumulates. The agent needs to remember where the conversation has been, what analyses it has already run, and what the user cares about. This is the gap between AI demos and AI products. And closing it requires a fundamental