OpenAI recently shared how their internal data agent works: [https://openai.com/index/inside-our-in-house-data-agent/ https://openai.com/index/inside-our-in-hou

OpenAI recently shared how their internal data agent works: [https://openai.com/index/inside-our-in-house-data-agent/ https://openai.com/index/inside-our-in-house-data-agent/](https://openai.com/index/inside-our-in-house-data-agent/ https://openai.com/index/inside-our-in-house-data-agent/) What interested me most wasn’t the model itself, but the surrounding system design: automated context retrieval, evaluation loops, and memory that improves the agent over time. I’ve been experimenting with recreating a similar setup, but with a different goal: making the configuration more accessible for any company. The result is an open-source YAML + Markdown framework where you define context sources, tools, and behavior explicitly instead of writing Python code. The idea is to make agent context easier to reason about, version, and iterate on, especially for data teams. Repo: [https://github.com/getnao/nao https://github.com/getnao/nao](https://github.com/getnao/nao https://github.com/getnao/nao) Would love feedback from people who have tried deploying analytics or data agents before.