Can AutoGen (Microsoft Agent Framework) help Data Science teams with automated EDA?

All answers to this question.

Absolutely. We’ve set up a "Data Science Lab" crew using the framework. The "Coder Agent" uses the AssistantAgent class to write Pandas and Seaborn code. What’s cool is the "Critic Agent" role. You can set its system message to be "a skeptical senior statistician." It will look at the generated p-values or R-squared scores and challenge the first agent's conclusions. This "adversarial" interaction often catches common pitfalls like data leakage or over-interpretation of noise that a single LLM prompt might miss entirely.

   Answered 2025-06-02 by Angela Stewart

Does it handle messy data well? Most real-world datasets are full of nulls and weird formatting. Does the agent just give up, or can it actually perform data cleaning?

   Answered 2025-06-04 by Edward Harris

• Edward, it’s actually better at cleaning than analysis sometimes! Because it can execute code, see the error or the "df.info()" output, and then rewrite the code to handle the nulls, it’s very resilient. It basically "debugs" the dataset in real-time until it has a clean version to work with. It’s like having a very patient intern.

     Commented 2025-06-06 by Raymond Scott

It’s a huge time-saver. It handles the 80% of boring visualization work, leaving us to do the 20% of high-level feature engineering and strategy.

   Answered 2025-06-08 by Patrick Nelson

• That 80/20 split is the dream. If I never have to write another boilerplate Matplotlib script again, I’ll be a happy camper!

     Commented 2025-06-10 by Melissa Young