AI Platforms vs Workflow Engines The Difference You Need to Know Now

n 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.

10 Key Takeaways:

A new technology stack for AI agents requires different layers beyond just the LLM.
Workflow engines (like Zapier, N8N, Make) are useful for connecting applications and creating simple, “if/then” logic.
Workflows can become “spaghetti” and unmanageable at scale, making them hard to maintain and prone to breaking.
A dedicated AI platform provides a central hub for oversight and management, preventing workflows from becoming brittle.
A platform separates the agent’s core logic (prompts, training, communication) from the workflow itself.
This separation is analogous to a website’s front-end and back-end, allowing for greater flexibility and control.
Platforms enable business users to make simple updates to agents without a developer having to modify hard-coded logic in a workflow.
A single agent on a platform can be deployed across many workflows, avoiding the nightmare of managing dozens of hard-coded copies.
AI platforms are designed for centralized management, security, simplicity, and speed, which are often sacrificed in hard-coded solutions.
The combination of a platform and a workflow engine maximizes ROI by providing both centralized control and complex sequencing capabilities.

WATCH: AI Platforms vs Workflow Engines The Difference You Need to Know Now