OpenAI Academy published a workspace agents guide aimed at teams that want to move beyond one-off ChatGPT prompts and into structured, repeatable workflows. The material covers how to design agents with three components — a trigger (scheduled or manual), a process with defined steps, and connected tools like Slack, CRMs, or ticketing systems.
The guide draws a clear line between agents and traditional API workflows. Traditional automation is deterministic: every step is hardcoded. Agents are probabilistic — they interpret context and make bounded decisions within instructions and guardrails. That distinction matters when you’re deciding whether to build a script or an agent.
Five workflow patterns get concrete treatment: briefings (pull data, distill, package), triage and routing (categorize inbound items, assign owners), analysis and recommendation (interpret data, draft a memo or deck), content creation (draft, edit, tailor, publish), and planning and coordination (build plans, update systems, notify people). Each pattern includes example use cases — a sales pipeline summary agent running every weekday at 8 AM from a CRM, or a product feedback triage agent triggered by a Slack form submission.
TIP
The guide recommends starting with low-risk requests when using an agent someone else built — simple inputs first, then review outputs before trusting it with higher-stakes work.
If you’re evaluating whether to build a workspace agent, the guide offers a practical filter: the work should be repeatable, structured enough that you can tell if the output is wrong, time-based or event-driven, and tool-dependent. If the task is exploratory or one-off, regular chat is still the better fit. That’s a useful heuristic for avoiding over-engineering.