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Workspace agents in ChatGPT now enable teams to manage complex projects collaboratively, leveraging AI to handle tasks and maintain organizational controls-marking a leap from solo GPT use to integrated team solutions.
OpenAI has just announced the introduction of workspace agents in ChatGPT, a significant step forward in AI-assisted team collaboration. These new agents, powered by Codex, are designed to handle complex tasks and long-running workflows within an organizational context, all while operating under the permissions and controls set by the organization.
Previously, GPTs (Generative Pre-trained Transformers) were primarily used for individual tasks, but they lacked the shared context and collaborative capabilities needed for team-based workflows. Workspace agents fill this gap by enabling teams to create, share, and refine AI-driven processes that can operate continuously in the cloud.
Sales Team Workflows
Software Review Agent
Product Feedback Routing Agent

Weekly Metrics Reporting Agent
Lead Outreach Agent
Third-Party Risk Management Agent
To create a workspace agent, follow these steps:
Workspace agents are currently available in research preview for ChatGPT Business, Enterprise, Edu, and Teachers plans. OpenAI emphasizes that GPTs will remain available while teams test these new agents with their workflows. They also plan to make it easy to convert existing GPTs into workspace agents soon.
The introduction of workspace agents in ChatGPT is a significant leap in AI-driven team collaboration. By automating complex tasks and maintaining shared context, these agents can significantly enhance productivity and efficiency within organizations. Whether you’re managing sales leads, reviewing software requests, or reporting weekly metrics, workspace agents are designed to streamline your workflows and keep your team aligned.
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About the author
Kai built ML infrastructure at a Bay Area startup before developing an obsession with transformer architectures and inference optimisation that eventually pulled him out of product work entirely. A stint at a compute research lab sharpened his instinct for what actually matters in a model release versus what is marketing. He writes from the inside — from the perspective of someone who has debugged the systems he is describing at three in the morning. He is allergic to hype and instinctively drawn to the unglamorous plumbing questions that everyone else skips over.
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23 April 2026
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