Eric Zhu (@ekzhu) 's Twitter Profile
Eric Zhu

@ekzhu

Working on AI Agents at Microsoft Research. Building AutoGen.

ID: 237181069

linkhttps://ekzhu.com calendar_today12-01-2011 07:48:11

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Eric Zhu (@ekzhu) 's Twitter Profile Photo

This is how you create a browser use agent in AutoGen. See our README (github.com/microsoft/auto…). We had browser use this time last year, it's wild that it took this long to take off. Follow us to see what we are cooking next: AutoGen

This is how you create a browser use agent in AutoGen. See our README (github.com/microsoft/auto…). We had browser use this time last year, it's wild that it took this long to take off. 

Follow us to see what we are cooking next: <a href="/pyautogen/">AutoGen</a>
Eric Zhu (@ekzhu) 's Twitter Profile Photo

The recent VSCode blog on Agent Mode (Preview) reveals its architecture design. It's a single agent with tools and a model. Read: code.visualstudio.com/blogs/2025/02/…

The recent VSCode blog on Agent Mode (Preview) reveals its architecture design. It's a single agent with tools and a model. Read: code.visualstudio.com/blogs/2025/02/…
Eric Zhu (@ekzhu) 's Twitter Profile Photo

Almost three months has passed since we officially released AutoGen v0.4 and 6 months since the preview release. We have made many new friends: - 1012 PRs since the v0.4 preview ♥️ - 35 Contributors 👯 👯‍♀️ - 1060 Issues closed 🙌 AutoGen welcomes your feedback and

John Langford (@johnclangford) 's Twitter Profile Photo

The Belief State Transformer edwardshu.com/bst-website/ is at ICLR this week. The BST objective efficiently creates compact belief states: summaries of the past sufficient for all future predictions. See the short talk: microsoft.com/en-us/research… and mgostIH for further discussion.

Eric Zhu (@ekzhu) 's Twitter Profile Photo

Tools form the environment for agents, and workbench makes it easier to create stateful environments. It’s a simple but effective abstraction.

Ahmed Awadallah (@ahmedhawadallah) 's Twitter Profile Photo

Introducing Phi-4-reasoning, adding reasoning models to the Phi family of SLMs. The model is trained with both supervised finetuning (using a carefully curated dataset of reasoning demonstration) and Reinforcement Learning. 📌Competitive results on reasoning benchmarks with

Introducing Phi-4-reasoning, adding reasoning models to the Phi family of SLMs.

The model is trained with both supervised finetuning (using a carefully curated dataset of reasoning demonstration) and Reinforcement Learning.

📌Competitive results on reasoning benchmarks with
AutoGen (@pyautogen) 's Twitter Profile Photo

✨ Human-in-the-loop, by design. Magentic-UI introduces four core HCI features to make human–agent collaboration more transparent, controllable, and adaptive: 🧑‍🤝‍🧑 Co-Planning 🤝 Co-Tasking 🛡️ Action Guards 🧠 Plan Learning Let’s dive into each 👇