ELi5 : AI Workflows vs AI Agents, Explained with LEGOs

Ever dumped a pile of LEGOs on the floor?

Yes?

Well then, you are already a step closer to understanding the difference between AI workflows and AI agents.

AI workflows: LEGO manual builds

An AI workflow is like opening a LEGO house kit and following the instruction manual from step 1 to step 12.

You know:

• exactly which piece snaps where
• the order of the steps
• and what the final house will look like

Nothing is left to chance.

AI workflows work the same way. They follow a fixed control path, a predefined sequence of steps.

But why do we even need workflows in the first place?

Why Models Alone Aren’t Enough

Models are really good at tasks like drafting emails, writing text messages, generating blog content, creating images, onverting text to voice, and other stuff

For example, if I ask an LLM:

“Hey, can you draft me a text to ask Sam out on a date?”
An LLM (based application) like ChatGPT or Gemini will do a great job...

...but sometimes, they kinda suck!

Say, I ask chat:
"When is my date with Sam?"
It won’t have a clue.

But what makes LLMs/Models kinda suck sometimes?

While, it's true that they have been trained on massive public datasets, they don’t have access to your personal or proprietary data. Stuff like your calendar, emails, company’s internal documents, etc.

So what’s the solution?
Give the model access to your data.(Not all of it. Be careful. Duh!)

Now, when the LLM gets questions around time like:
“When is my date with Sam?”
OR
"When is my lunch?"
OR
"When is my meeting?"

it will:

1. Query your calendar
2. Extract the relevant event
3. Summarize it
4. Respond to you

That’s an AI workflow!

Note that over here the model is NOT deciding what to do, it’s following a pre-wired path:
Input → Retrieve → Process → Respond

Just like a LEGO manual, the logic and path is fixed!

What makes workflows awesome

AI workflows are awesome because of:

• Predictable behavior
• Easy to reason about
• Cheap and efficient (Like really cost friendly)
• Same input → same output

You have a ballpark figure of how many “pieces” (API calls, LLM calls, compute) it will take.

The downside

If you didn’t plan for a step, the system breaks.

Just like realizing mid-build that the manual requires a rare LEGO piece you lost under the couch. Everything stops until a human fixes it. :/

Say, in the AI workflow above, you ask:
“What should I wear for my date, given the weather?”

The workflow will fail! Not because the question is hard, but because:

• it doesn’t have access to a weather API
• it doesn’t know how to fetch outfits
• it wasn’t designed for this path

Sure, you can fix this by adding a weather API, adding an image generation model, wiring everything together

But no matter how many modules you add, it’s still just a workflow.

It is still a fixed, predefined path.

No matter how many extra tools you glue on, the workflow still can’t decide to change the plan. When you need the system to rethink the plan itself, you don’t need more steps, you need something with a goal and autonomy. That’s where agents come in.

AI Agents: Free Builds With a Goal

An AI agent is like dumping a pile of LEGOs in front of a kid and saying:

“Build me something I can live in."

You don’t give the kid instructions. You give them a goal.

The kid then:

• inspects the available pieces
• decides to build a house
• realizes they’re missing roof tiles
• pivots to a cabin… or a cave

They reason their way to the goal using whatever resources they have.

Similarly, with agents, you don’t give the Model a pre defined path, you give:

• a goal
• a set of tools (APIs, vector databases, workflows, search)
• permission to decide what to do next

When you give models tools, a goal, and permission to decide what to do next, that’s when they start acting like agents.

In a workflow, you decide this once at design time. In an agent, the LLM decides this at runtime.

But the awesomeness of agents comes at a cost

Every decision , “Should I search the web?” , “Should I call this API?”, “Do I need another refinement loop?” is another LLM reasoning step.

Think of it like hiring a brilliant architect:

• incredible ideas
• lots of sketches
• very expensive

Agents rarely crash outright. instead, they might give you something technically valid but very wrong.

Like a LEGO jail, when all you wanted was a small cabin.

So When Should You Use Which?

If you need certainty and repeatability, workflows are your friend.
You know exactly what pieces exist, exactly how they fit together, and exactly how the system behaves. Basically, when you need a factory.

If you need adaptability in messy environments, agents make sense.
They can reason around missing pieces, try alternative approaches, and still deliver something when the path isn’t clear.

But the most practical pattern is hybrid.

• Let workflows handle the predictable assembly line
• Drop agents into the steps that truly need flexible reasoning

This approach is called, Agentic Workflows, and it’s how most real-world AI systems are being built today.

Manual where possible. Free build where necessary. Just like LEGOs.

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Rolling Credits:

• YouTube videos
• LLMs
• Reddit