content(library): restore original Zapier/Make wording in agentic-workflow post

Author: icecrasher321

apps/sim/content/library/what-is-an-agentic-workflow/index.mdx

@@ -15,7 +15,7 @@ faq:
   - q: "Is agentic AI the same as automation?"
     a: "No. Agentic AI chooses actions at runtime, while traditional automation follows human-authored rules. Sim supports both modes in one workflow graph, so you can use reasoning for ambiguous work and fixed logic for predictable steps."
   - q: "Can Zapier or Make build agentic workflows?"
-    a: "The categories increasingly overlap as automation platforms add AI capabilities. Sim treats Agent blocks as first-class parts of the execution graph alongside deterministic blocks. The right choice depends on whether reasoning serves as one isolated step or directs several parts of a workflow."
+    a: "Zapier and Make can include AI or agent steps within their trigger-action workflows. Sim instead treats Agent blocks as first-class parts of the execution graph alongside deterministic blocks. Your choice depends on whether reasoning serves as one isolated step or directs several parts of the workflow."
   - q: "What is the difference between an AI agent and an agentic workflow?"
     a: "An AI agent reasons and uses tools, while an agentic workflow coordinates that reasoning with triggers, data, control flow, and other steps. The workflow gives an agent defined inputs, boundaries, and operational context."
   - q: "Do agentic workflows replace deterministic automation?"
@@ -69,7 +69,7 @@ Agentic reasoning fits steps that require judgment under ambiguity. A model can
 
 A support workflow shows how the modes can divide responsibility. Deterministic validation can verify the ticket fields before an agent interprets the customer’s message. The agent can recommend a response and select a relevant tool, while fixed rules route high-value refunds to human approval. Each mode handles the failure it can control most effectively. Practical [support ticket triage patterns](https://www.sim.ai/library/best-ai-agents-support-ticket-triage) apply the same division of responsibility.
 
-Architecture determines how closely those modes can cooperate. Automation-first platforms generally place AI steps inside a trigger-action graph whose surrounding paths remain human-authored, as reflected in the official guides to [adding AI steps in Zapier](https://help.zapier.com/hc/en-us/articles/16276574838925-Use-AI-by-Zapier-in-your-Zap) and [building scenarios in Make](https://help.make.com/scenario-editor). The model can reason within its assigned step, but predefined edges usually control where execution goes afterward. Builders must add branches around the AI output if they want the wider workflow to react differently.
+Architecture determines how closely those modes can cooperate. Automation-first platforms such as Zapier and Make generally place AI steps inside a trigger-action graph whose surrounding paths remain human-authored, as reflected in the official guides to [adding AI steps in Zapier](https://help.zapier.com/hc/en-us/articles/16276574838925-Use-AI-by-Zapier-in-your-Zap) and [building scenarios in Make](https://help.make.com/scenario-editor). The model can reason within its assigned step, but predefined edges usually control where execution goes afterward. Builders must add branches around the AI output if they want the wider workflow to react differently.
 
 A reasoning-native graph treats the model as a node that can choose tools and return structured decisions to deterministic controls. Fixed blocks can validate the model’s output, restrict available actions, and require approval before execution continues. You can then give the model discretion only where the task needs it, while preserving predictable behavior around sensitive operations.