If you're building tools using ChatGPT, Zapier, n8n, or other automation platforms, here's a hard truth:
You’re probably building a workflow — not an AI agent.

In the AI space, these terms get thrown around a lot. But mixing them up isn't just semantics — it can seriously impact how you design, scale, and evaluate your systems.

🚦 Workflow vs. Agent: What's the Difference?
Workflows are deterministic. They follow a set of predefined steps and instructions.
Think: “If X happens, do Y.”
Even if GPT is involved, if you're feeding it static prompts or scripts, it’s still a workflow.

AI Agents, on the other hand, are autonomous systems.
They reason, plan, adapt, and often maintain memory and context.
They can decide how to solve a task — not just what to do next.

Why This Distinction Matters
As AI systems evolve, builders need clarity. If you mislabel a workflow as an agent, you might:

Overestimate its capabilities

Underprepare for edge cases

Miscommunicate value to users or stakeholders

In our latest YouTube video, we break this down in simple, real-world terms — with examples like customer support bots, schedulers, and automation flows.

👉 Whether you're deep into agentic design or just getting started with automation tools, this will help you build smarter.

🎥 Watch the full video here: Stop Calling Your Workflow an AI Agent – Here's Why It Matters