"When you automate something, you typically provide three things: Define workflow. A sequence of steps that a system should follow to accomplish a particular task at hand. Set a trigger. Define the condition when you trigger this workflow. This can be a certain event or a time of the day. For example, for the mail analyzer flow, a trigger can be a new incoming mail to your inbox."
"Define rules and success criteria for specific steps. You manually specify the criteria for step completion. Automation might seem like a powerful concept (and it does actually), but it has one major downside - you are in charge of defining the workflow a system will follow, and you define specific criteria for step completion. For example, if you create automation for email analysis, you will define specific criteria for an email to mark it for a particular group. You still the one who runs the show."
Automation requires explicit definition of a workflow, a trigger, and manual rules or success criteria for each step, which keeps control firmly with the human designer. AI agents differ by acting independently to accomplish tasks on behalf of users, using large language models to infer criteria, make judgments, and adapt actions without fully prespecified step-by-step instructions. AI agents enable handling ambiguous, dynamic, or judgment-heavy tasks where static automation falls short. Practical adoption includes selecting agent-appropriate use cases and leveraging popular tools like ChatGPT to prototype, iterate, and evaluate agent behavior while maintaining oversight and safeguards.
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