In this episode we discuss the common problem of conflating AI platforms with workflow engines like Zapier or N8N. They explain that a new "stack" for agentic solutions requires more than just the large language model (LLM) itself. While workflow engines are useful for connecting apps and creating simple "if/then" logic, they can quickly become brittle and unmanageable when dealing with the complexities of managing multiple AI agents and their underlying conversational logic.The solution is adopting a dedicated AI platform for centralized management. Rich uses the analogy of separating a website's front-end from its back-end database to illustrate the importance of separating the agent's management (platform) from the workflow logic. This approach prevents the need for a developer to manually adjust hard-coded logic every time a business user wants to make a simple tweak to an agent's instructions, ensuring greater flexibility and fault tolerance.We also discuss the benefits of this integrated approach for scalability and control. A platform allows a single, well-maintained agent to be used across dozens of workflows, preventing the "nightmare" of having to update 30 hard-coded copies of an agent every time a model is upgraded. The hosts stress that while workflow engines are a valuable part of the stack, an AI platform provides the centralized management, security, simplicity, and speed needed to build a scalable and sustainable AI workforce.