Casey Chu

ChatGPT can now do work for you using its own computer. Introducing ChatGPT agent—a unified agentic system combining Operator’s action-taking remote browser, deep research’s web synthesis, and ChatGPT’s conversational strengths.

Introducing shopping research, a new experience in ChatGPT that does the research to help you find the right products. It’s everything you like about deep research but with an interactive interface to help you make smarter purchasing decisions.

We also found that, when allowed 16 tries per problem, ChatGPT agent’s score grew from 27% to 49% on the tier 1-3 set. This suggests that better prompting or scaffolding might result in better performance from current models.

Just launched ChatGPT Agent (sorry GPT-5 waiters, it is coming!), the most capable AI agent model to date! It has been such an honor to be part of a crazy sprint to get this amazing model trained and shipped together with an absolutely gem team (@isafulf , @caseychu9 , @EdwardSun0909 , @josh_tobin_ Yash Kumar and many more)! I am so proud of this project, so I want to share some highlights, personal takes and lessons learned while working on it: 1. Used for research 📕 + actions 💻 + slides generation: Deep Research can do research. Operator can take actions for you. ChatGPT Agent can do both at the same time! E.g. you can ask it to make a plan for a trip to Hawaii, find good deals on hotels and flights, and book them on your behalf using its own computer! It can also generate slides! 2. Power of end-to-end RL: How do we build it? You guess it right! It is us, @OpenAI RL diehards. You are probably tired of hearing about RL scaling. Me, too. But when I feel its power first-hand, its effectiveness and data efficiency still shock me and feel like magic 🪄. 3. First OpenAI model of high biorisk 💀: Not sure this is something I should proud of or not :) For an ex-AI bio PhD researcher like me, this is something a bit personal. One one hand, many of my biomedicine researcher friends tell me that AI agents have significantly helped with their research. On the other hand, such a capable model can amplify the risk of malicious actors building bioweapons. Our safety team has done incredible work to mitigate the risks. 4. Collaboration with users 👪 is core: We want our AI to augment and enhance humans, not to replace them, so we work hard to make the model good at collaborating with the user. You can type a message at anytime to interrupt it and steer it to new directions. The model will always confirm with you before taking actions like buying things for you or deleting a file on your google drive. And the model will ask clarification questions only when it needs more clarity from you! 5. How to generate good slides: As in other cases, writing a well-specified prompt always helps! Also try first telling it to generate a report, then convert the report into slides! 6. Real-world performance > benchmark chasing: One thing outside people may not know about us is how little attention we pay to external benchmarks during the model dev process. We do not focus on hill-climbing on them, and we do not care that much about how we end up on the leaderboard. That said, as a byproduct of our pursuit to great real-world performance and true intelligence, ChatGPT Agent does crush many benchmarks! Wanna learn more? Read our blog linked in the end! In the end, I want to shout out to my amazing team again. These extremely talented and kind people are the reason why OpenAI is constantly making magic like this! ❤️ Also please try ChatGPT Agent and give us feedback! You can reply here in the thread or my DM is open. This is just the start. We will continue working hard towards more and more capable super-human AI agents! 🤖 openai.com/index/introducing…

We're hiring for a new team @OpenAI: Agent Robustness and Control Our goal is to make sure our agents safe and secure during training and deployment. Want to work on some of the hardest problems in AI today? Apply via link in reply or DM me!