Workspace agents

Most ChatGPT users already know how to use AI for one-off tasks—like drafting, summarizing, brainstorming, or answering questions. The next phase of AI use is broader and more embedded in day-to-day work. Instead of helping with isolated moments, AI is increasingly being used to support repeatable workflows that depend on shared systems, standard handoffs, consistent outputs, and real-world constraints like timing, accuracy, and process.

That’s where workspace agents in ChatGPT fit. They’re designed to be used for repeatable workflows—work you’d otherwise do manually, re-explaining the steps each time, and copying information between tools. Learn more about workspace agents in our blog post .

If you’re new to agent building, let’s focus on the core concepts first so when you start building, you’ll know how to set up your workspace agent for consistent results.

The examples below can be thought of as agent workflow patterns : common, repeatable patterns of work that show up across teams and functions. Each one represents a core type of workflow an agent can carry out. The specific tools, data, and outputs may vary, but the underlying pattern stays consistent.

Start in plain language: In the agent builder chat, describe the job the agent should do, what a successful outcome looks like, and any constraints it needs to follow. The builder can translate this into a clear workflow with defined steps, which you can keep refining in chat or edit directly in the workflow and instructions.

Choose tools and connectors: Select the approved apps the agent can use to complete the workflow. You can also describe the systems the agent needs to access, and the builder will guide you through adding and authenticating them.

Structured: There’s a clear format for the output (so you can tell if the agent is doing a good job)

Support: Turn feedback into bugs, feature requests, or follow-ups.

Update the instructions directly: Make small, specific changes in the editor, such as clarifying a step, adjusting the output format, or adding a constraint.

Generally speaking, an agent is a system that carries out a task with three components: a trigger, a process that may include specialized skills, and tools or systems it can connect to.

Reviewing inputs, checking for missing information, drafting an output, and handing it off or taking the next action.

The approved tools and integrations the agent can use to gather information and, if allowed, take actions.