Overview
OpenAI’s use-case and agent guides form a decision path from workplace friction to controlled workflow execution: diagnose the problem, classify the needed assistance, weigh impact against effort, map inputs, handoffs, permissions, and failure costs, then choose assistance, repeatable automation, or an agent. The guidance supports disciplined design, not universal ROI, accuracy, or autonomy thresholds; local evidence must determine what to scale and where people intervene.
Learning outcomes
- Start with workplace friction, not a product, and state what a good result must preserve.
- The six primitives describe needed assistance; they do not establish value, priority, safety, or agent fit.
- Impact and effort are revisable judgments that include data, permissions, testing, adoption, and maintenance.
- Increase independence only when contextual control is necessary, testable, and bounded by guardrails and human intervention.
Keywords
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PlansFAQ
How should OpenAI’s guides be used to choose an AI workflow?
Use them as a sequence: diagnose friction, classify assistance, prioritize impact and effort, map the workflow, then choose the least independent design that can work.
When does an AI workflow need an agent instead of automation?
An agent is worth investigating when contextual judgment and exceptions, hard-to-maintain rules, or unstructured information make fixed transitions insufficient; stable, testable rules may favor deterministic software.
What should an organization evaluate before scaling an AI use case?
Evaluate the outcome, scope, inputs, reviewers, permissions, failure costs, existing tools, and the effort of data preparation, testing, adoption, support, and maintenance.
How can an AI agent remain under human control?
Keep tools and instructions explicit, limit permissions, evaluate decisions and handoffs, add layered guardrails, and return sensitive or failed work to a person.