AI Workflows vs. AI Agents: The Surprising Difference Isn't What You Think

 

AI Workflows vs. AI Agents: The Surprising Difference Isn't What You Think

If you've spent any time online lately, you've probably felt like you're drowning in a sea of AI buzzwords. Terms like "AI agents," "agentic capabilities," and "agentic workflows" are everywhere, promising the next great leap in artificial intelligence. The problem is that most explanations are either too technical for the average user or too basic to be useful.

This article cuts through the noise. We'll follow a simple one, two, three learning path, building on concepts you already understand. We'll start at Level 1 with the chatbots you already use, move to Level 2 to understand AI workflows, and finally arrive at Level 3 with a clear definition of true AI agents. Our goal is to distill the most important and surprising takeaways from this evolution, using practical examples to show you what really matters.

Let's break it down.

Level 1: The Foundation (Large Language Models)

We begin at Level 1 with something you're already familiar with: Large Language Models, or LLMs. These are the engines behind popular AI chatbots like ChatGPT, Google Gemini, and Claude. They are fantastic at generating and editing text.

You give an LLM an input (a prompt), and it produces an output based on its training data. For example, if you ask it to draft a polite email, it does a great job. But what if you ask, "When is my next coffee chat?" The LLM will fail. Why? Because it doesn't have access to your personal calendar.

This highlights two key traits of LLMs you need to remember:

1. They have limited knowledge of proprietary information (like your personal data).
2. They are passive. They wait for your prompt and then respond.

Level 2: From Following Orders to Following a Path (AI Workflows)

Moving to Level 2, we have AI workflows. This is where we start telling the LLM to follow a predefined path set by a human.

Imagine using a tool like make.com to create an automation. You might set up a workflow that compiles news articles from a Google Sheet, sends them to Perplexity for summarization, and then uses Claude to draft social media posts.

This is an AI workflow because it follows a rigid, human-designed path: Step one, you do this. Step two, you do this. Step three, you do this. Even if the process has hundreds of steps, it remains a workflow. If you test the output and think the LinkedIn post isn't funny enough, you, the human, have to manually go back and rewrite the prompt for Claude. This trial-and-error iteration is all on you.

This brings us to the most critical distinction in this entire discussion. The leap from a Level 2 workflow to a Level 3 agent isn't about adding more steps or complexity. It's about who is in control.

The one massive change that has to happen in order for this AI workflow to become an AI agent is for me, the human decision maker, to be replaced by an LLM.

This is a counter-intuitive but crucial distinction. We often mistake complex, multi-step automation for true intelligence. But as long as a human is setting the rigid path and making all the key decisions, it's still just a workflow.

Level 3: From Following a Path to Charting the Course (AI Agents)

An agent can reason, act, and optimize its own process to achieve a goal.

This is where things get really interesting. At Level 3, the AI agent isn't just following a path—it's charting the course. It can critique and improve its own work autonomously.

Remember our workflow where the human had to keep rewriting the prompt to make a LinkedIn post funnier? An AI agent can perform this iterative process by itself. Faced with the goal of creating a "good" LinkedIn post, it might have an internal monologue like this:

"Okay, I've drafted V1 of a LinkedIn post. How do I make sure it's good? Oh, I know. I'll add another step where an LLM will critique the post based on LinkedIn best practices. And let's repeat this until the best practices criteria are all met."

This capability is a game-changer. It transforms the AI from a static tool that follows a fixed script into a dynamic system that can self-correct, learn, and adapt its approach to better achieve the objective it was given.

Those Intimidating Buzzwords Are Simpler Than You Think

The field of AI is filled with jargon, but many of the terms describe straightforward concepts. Let's demystify two of the biggest ones you'll see.

• Pro Tip: Retrieval-Augmented Generation (RAG) is just a type of AI workflow. RAG is a fancy term for a simple process: helping an AI "look things up" from an external source before it answers. The best way to understand it is with a story.
1. You ask a Level 1 LLM, "When is my coffee chat?" It fails because it can't see your calendar.
2. You create a Level 2 workflow and tell the AI, "When I ask about an event, always search my Google Calendar first." Now, when you ask about your coffee chat, it works!
3. But then you ask a follow-up: "What will the weather be like that day?" It fails again, because the rigid path you created only told it to look at the calendar, not a weather service.
• That process of giving an AI a tool to retrieve specific information (like your calendar) is RAG. It's an essential component, but it's still a workflow where the human defines the rules.
• The ReAct Framework is exactly what it sounds like. This is the most common configuration for AI agents, and the name is a simple acronym for the two things an agent must do to function. It must Reason (think about the best approach to achieve a goal) and it must Act (use tools to execute its plan). That's it. An agent thinks, then it does.

Sounds simple once we break it down, right?

Conclusion: From Following Orders to Setting the Strategy

We've traced the evolution of AI from Level 1 passive LLMs that respond to our inputs, to Level 2 AI workflows that follow our predefined rules, and finally to Level 3 true AI agents that can reason, act, and iterate on their own.

The most significant evolution is the promotion of the AI from an "employee" that meticulously follows instructions to a "decision maker" that can devise its own strategy to achieve a goal. This shift from following a path to charting the course is the true essence of an AI agent.

Now that you understand the difference, what is the first goal you would give to a true AI agent?