Nathaniel Whittemore

The idea is that organizations can set permissions and controls, and then individuals and teams in those organizations can spin up their own agents.

These agents are meant to do a lot of the things that make up the day-to-day of knowledge work.

Responding to messages, preparing reports, things like that.

They are cloud-based so they can work even when you're not on ChatGPT, and they are designed for team use from the beginning so that once someone builds them, it can be shared with an organization and used in shared spaces like ChatGPT Enterprise or in Slack.

Now, in some ways they are an obvious evolution of custom GPTs.

In fact, in many ways, these workspace agents are kind of what I think people imagined custom GPTs would be.

It was obviously just a little bit too early for this full breadth of use cases to really be useful with that earlier version.

Simon Smith writes, I think Simon's framing of them as sitting between custom GPTs and the complexity of OpenClaw-style agents is a pretty good shorthand.

OpenAI gave five examples of workspace agents that teams internally had built.

A software reviewer to review employee software requests, including checking them for things like adherence to approved tools and policies.

A product feedback router agent that could monitor Slack or other support channels and turn feedback into prioritized tickets.

weekly metrics reporter agents that can pull data on a regular schedule and turn that into shareable charts and summaries.

And they also talked about outreach agents, which is something that I feel like at this point every company that's trying to be at least even a little bit agentic has thought about, i.e.

agents that do things like research inbound leads, update your CRM, etc.

And the last example they gave was a third-party risk manager that could research vendors assessing a bunch of signals and ultimately produce a report with recommendations.

Workspace Agents also has templates in functional areas like finance, sales, and marketing.

Now, when Simon was describing all these features they have access to, because they are powered by Codex in the cloud, which means they have access to the same sort of files, code, tools, and memory that Codex does.

This is also why they are a difference in kind, not just a difference in scale from custom GPTs.

As OpenAI puts it, agents do more than answer a prompt.

They can write or run code, use connected apps, remember what they've learned, and continue work across multiple steps.