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Surprise! Operator and deep research had a baby, and it’s called ChatGPT Agent.
You can imagine the meet-cute happening in a dark, humming server room in Texas:
Deep research—highly verbal, long-winded, and bolted to a server rack—completing sentence 97 of a 10,000-word report on the hiring landscape for AI engineers circa 2024 to 2025. It stares at the LinkedIn login screen, sweating, thinking… thinking… thinking… in circles. It can think, but, alas, it can’t do.
Operator streams in, equipped with the high personality and low IQ of a golden retriever, but also—miraculously—with hands. It comes up behind deep research, leans in close, and presses “Login.”
“Done!” says Operator with a twinkle.
The rest, as they say, is artificial history. (Reader, they interfaced.)
Their progeny, ChatGPT Agent, is like deep research in that it can do long-running research tasks. And it’s like Operator in that it can use a computer, including a browser, a terminal, and LibreOffice. So it can do a host of things that neither of its parents could do on their own:
- Navigate complex multi-step workflows: Read all of your technical support emails, identify product promoters, search for them on LinkedIn, and synthesize customer archetypes
- Transform raw business data into executive presentations: Analyze P&L spreadsheets and performance metrics, and generate PowerPoint decks with insights
- Conduct comprehensive UX audits: Browse through multiple websites, document user flows, and compile detailed usability reports
- Create intelligence briefings from real-time data: Scan news sites, research papers, and forums to produce daily executive summaries on specific topics
- Handle authentication and dynamic content: Log into password-protected sites, navigate JavaScript-heavy pages, and extract data from behind paywalls
After a few days of using it, here’s my Vibe Check. We’ll go through how it works, the Reach Test (is it a habit?), and what it means for the competitive landscape. Let’s dive in.
A tour of ChatGPT Agent
We’ll start our tour of ChatGPT Agent with an example. Our email management product, Cora, has been growing quickly, and I wanted to understand more about the current state of the customer base. I wanted to know:
- Why people love us (what the job to be done is)
- Who loves us (customer archetype)
- The biggest complaints
So I started a new ChatGPT chat and added “ChatGPT Agent” as a tool, similar to how you invoke deep research:
Then, I prompted ChatGPT Agent to read two months’ worth of support emails and feedback forum posts. I instructed it to look at our support email account and all of the support forum posts, and asked it to tell me who uses the product, who loves it, and what the complaints are.
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Just came from Lenny's Podcast, I see now how an important focus on writing outputs extremely well crafted ideas. Thanks for this, very helpful to get another perspective on this.
I previously posted a respectful counterpoint here about the fluid UX of ChatGPT and the inconsistent framing of ‘toy vs. interface to the web.’ It seems to have quietly disappeared—though the post still shows 2 comments. Ironic, given the piece’s final line about intermediation. Looking forward to a platform that values open commentary as much as open agents.