Why AI Agents Are the Future (and Why You’re Confused)
You ever stare at your screen, ask ChatGPT a question, and feel like it’s just… guessing?
Yeah. Me too.
That's because most AI tools—what we call large language models—are super powerful, but also super passive. They don't DO things (no action), and they lack context. They don't know what's on your calendar, in your Google Drive, your notes, or your to-do list...
...and the problem is, when they don't know, they usually “imagine” things that don't exist.
Try to ask ChatGPT when your next meeting is (in the middle of a conversation).
It'll pause. Then give you a very confident answer.
That's completely wrong.
Why?
Because ChatGPT doesn't know your calendar, nor how to access it, and it’s optimized to please you.
This limitation is the main motivation behind the concept of "Agents."
There's a huge difference between "AI" and "AI Agents."
AI Agent = AI + context + autonomous decision + action
And if you've ever used an AI tool, this difference matters more than you think.
As AI agents gain popularity and new protocols emerge, I want to dedicate this letter to a simple breakdown of the differences between AI and AI agents in simple terms.
Many people are asking me this question, and I've noticed that even "more advanced" people tend to confuse the concepts of AI assistants, AI workflows, and AI agents.
If you're already familiar with agents, consider this a helpful refresher.
Let’s dive in.
Level 1: Just Your Friendly AI Chatbot
Large Language Models (LLMs) are where most of us start.
ChatGPT, Claude, Gemini—these are all LLMs.
They’re great at one thing:
You give them an input. They give you an output.
Example:
🧠 You: "Write me a catchy slogan for my new coffee shop."
🤖 AI: "Where Every Bean Tells a Story, Every Sip Writes History, Every Morning Becomes an Epic Tale of Caffeinated Wonder in Your Journey Through Life's Greatest Adventures..." (somehow turns into a novel when all you needed was "Great Coffee, Better Mornings").
Also recently, we've all experienced the additional "reasoning" layer that makes the process way more smarter by letting the AI system reflect on its own output a couple of times before generating the final result.
It’s like a super helpful intern that read the internet and knows how to write very nicely.
But it has two major limitations:
1. No access to your private info (or any context)
It doesn’t know your calendar. Or your documents. Or your Slack convos.
2. It’s passive (no action)
It sits there.
Waits for your prompt.
Responds.
That’s it.
So if you say:
"Hey, what's in my photo library from last summer?"
It won't know. Because it can't access anything unless you tell it exactly what to do.
Level 2: AI Workflows
Now let’s level up.
Imagine I build a system where the AI checks my calendar (or specific documents/context) before answering any question—systematically.
Suddenly, we have something smarter.
The AI is not just replying. It’s doing stuff in a sequence.
Access context → fetch data → respond.
This is called an AI workflow.
It’s like giving the intern a checklist.
And as long as it follows your checklist, it works great.
A typical example of this are AI assistants with a specific context. When you create a GPT on ChatGPT, you create the following workflow:
Access instructions/sources → fetch data → respond.
This is a simple, widely used workflow.
Here's another more "sophisticated" example.
Example: Daily AI-Powered Social Posts
Here's a workflow I actually built using Flowise:
📊 Step 1: Monitor my blog for new posts
📝 Step 2: Extract topics and summaries into a dedicated Google Sheet
🤖 Step 3: Use Claude Assistant to craft LinkedIn posts to promote my blog posts
📅 Step 4: Schedule posts at optimal times throughout the week
✅ Result: Each morning I have a queue of teasers for my tech blog posts to post.
Next it’s the human touch. I read the posts add my inputs then post.
But this process is still quite “rigid”: the AI follows my rules and there's no decision-making involved—no "agency."
If I'm not okay with the results, I need to go back to my workflow and adjust my rules manually… The system relies on my judgement for better or worse performance.
What if the AI could decide the workflow depending on the situation?
That's where Agents enter the game.
Level 3: AI Agents
Here’s where it gets powerful.
Everything we’ve done so far has one thing in common:
The human (you or me) is still the boss.
(AI Workflow = AI + context + action)
The moment an AI becomes the decision-maker in the workflow?
That’s an AI agent.
So… what’s the difference?
AI Workflow:
You tell the AI what to do and when to do it.
Like this:
Input → Step 1 → Step 2 → Output
AI Agent:
You give the AI a goal.
And it figures out the steps on its own.
It reasons.
It acts.
It iterates.
Goal → Reason → Act → Observe → Adjust → Output
My Workflow vs. an AI Agent
Let’s revisit my daily blog posts automation example.
In my current setup:
- I define the steps.
- I decide when something isn’t good.
- I revise the prompt and logic.
- The system runs on My “new rules.”
But what if…
- The AI decides it needs to fetch new blog posts and how.
- Chooses the best tool/way for summarizing.
- Writes a first draft.
- Then asks another AI to critique it.
- Improves it based on feedback.
- And does this cycle until the post meets defined quality standards?
In the first case (AI Workflow) I define WHAT I want and HOW to do it.
In the second case (AI Agent) I just define WHAT I want.
Now it’s not a tool.
It’s a thinking, doing, improving system.
That’s an AI agent.
Real Example: Let's Plan Some Romance in Japan!
Here's a fun one. Let's say you want an AI to be your personal travel agent for Japan - planning everything from where to eat to where to pop the big question (yes, that question!).
I tried this with Manus (one of their showcase examples, actually).
Here's what you threw at it:
"Help me plan a 7-day Japan adventure from Seattle (April 15-23, $2500-5000 budget) for me and my soon-to-be-fiancée. We're history nerds who love finding hidden spots and soaking in Japanese culture - think kendo matches, tea ceremonies, and zen vibes. Gotta see those famous Nara deer! Oh, and I need the perfect spot to propose. Could you whip up an itinerary and a handy digital guide with maps, Japanese phrases, and insider tips?"
Now watch how this AI agent tackled it:
1. First: Breaking It Down
Like any good travel agent, it started by dissecting the challenge: Japan basics ✓ Hidden gems ✓ Cultural spots ✓ Perfect proposal location ✓ All while keeping those dollars in check!
2. Next: Deep-Dive Research
The agent went full detective mode - scanning through tons of info about Tokyo, Kyoto, and Nara. When it hit roadblocks (like those pesky CAPTCHAs), it just found another way. Clever little thing!
3. Then: Putting It All Together
Here's where it got really interesting. The agent created:
- A perfect 7-day mix of temples, traditions, and adventures
- Three absolutely romantic proposal spots (during cherry blossom season - smooth move, AI!)
- A mobile-friendly travel guide packed with maps, Japanese phrases, and local tips
4. Finally: The Magic Moment
The end result? Not just a boring PDF, but a complete digital travel companion.
You give the agents no rules on how to do it. You just describe WHAT you want.
At this stage the concept of an AI Agent should be cristal clear for you.
You can check out other examples by visiting the use case gallery section of Manus AI. That's a great way to see what agents are now capable of doing.
How to Know Which One You Need
Key Takeaways (Tattoo These on Your AI Brain)
- LLMs respond to prompts. Nothing more.
- Workflows follow a checklist built by you.
- Agents build their own checklist and improve it.
If you’re manually revising, deciding, testing—you’re the agent.
To stop being the agent, let the AI reason, act, and iterate.
Final Thoughts
I hope this guide helped demystify AI agents and their incredible potential. By now, you should have a crystal-clear understanding of what sets agents apart from regular AI assistants and workflows.
Here's my battle-tested process for building effective AI agents:
- First, manually execute using AI assistants and document your prompts, refining through repetition
- Map out your decision-making logic at each step
- Convert it into a workflow and iterate until it's smooth
- Gradually introduce AI autonomy into key decision points
- Monitor and optimize performance
To be honest, I first doubted agents until I created a few that completely changed my days. I currently have 4-5 agents running on Flowise that save me 3-4 hours every single day: generating blog post drafts, content, ideas of all sorts, plans…
That’s the breakdown.
The next time you catch yourself repeating a sequence of AI tasks, pause.
Ask yourself:
Can this become a workflow?
Can this evolve into an agent?
Tools like Flowise (a bit pricey, but beginner-friendly) or Manus make it easier than you think.
Start building systems that think, act, and improve—without you.
That’s where the real leverage lives.
What kind of agent would you build?
Hit reply—I read every one.
Until next time,
— Charafeddine