WTF are AI Agents?

“AI agents this, AI agents that…” 
But seriously — what are AI agents?

If terms like agentic workflows, autonomous agents, RAG + ReAct make your brain go fuzzy — you’re not alone. It sounds complex, but the core idea is actually pretty simple. (Trust us!)

You’ve probably Googled “what are AI agents?” and hit one of three walls:
A. Too technical
B. Too basic
C. Just plain confusing

If you're like us, with no coding background, using AI tools daily, and curious to actually understand how AI agents work, this newsletter is for you.

We’re breaking it down for YOU in 3 levels using real-world examples you’ll actually recognize:

1. LLMs (like ChatGPT)

2. AI Workflows

3. AI Agents

Level 1 – Large Language Models (LLMs)

LLMs like ChatGPT, Claude, and Gemini are everywhere. They’re awesome at:

• Drafting emails

• Summarizing articles

• Explaining quantum physics like you’re 5 (Imagine having that in school... wild.)

You give them a prompt → they give you an output.

But here’s the catch: they’re passive — kind of like your boss waiting for you to do something.

Ask ChatGPT “When’s my next meeting?” and it’ll fail—because it doesn’t know your calendar.

Key Traits of LLMs:

• No access to private or real-time data

• Wait for you to act—they don’t initiate (Kind of like that trip with your friends... won’t happen unless you start the group chat.)

Level 2 – AI Workflows

Now, let’s say you connect ChatGPT to your calendar.
You set up a workflow that says:
“If I ask about a meeting, check Google Calendar first.”

Boom, your chatbot can answer correctly.

Want to get more detailed? Add more steps:
Like pulling the weather, generating audio, and posting it to Slack?

Very cool. But here’s the thing: it’s still a workflow — you’re still doing the thinking.

Why?
Because YOU are the one creating the logic.

Some on-ground examples:

• Google Sheets stores article links.

• Perplexity summarizes them.

• Claude writes a LinkedIn post.

• Scheduled to run every day at 8AM.
  

If the post isn’t funny enough (and you're naturally hilarious), you go back and tweak the prompt manually. That’s still a workflow, not an agent.

TL;DR – Workflows are:

• Predefined paths, built by humans

• Good at automation

• Still rely on you for decision-making (You are still the mind behind it all)

Level 3 – AI Agents

Here’s the shift: AI agents don’t just follow steps — they make decisions.

Let’s say you want to generate a LinkedIn post based on current news.
Unlike a basic workflow, an agent doesn’t just run a script it:

• Reasons for the best way to approach the task

• Takes action while fetching data, writing content, & revising

• Iterates until the result meets the goal

No more tweaking prompts or reverse-engineering outcomes.
The agent critiques its own work and improves autonomously. (Basically, AI agents are like the principle of AI)

That’s the ReAct framework:
Reason → Act → Iterate

Real-World AI Agent Example

AI legend Andrew Ng demoed an AI vision agent:
You search “skier,” and the agent:

• Thinks: “What does a skier look like?”

• Reviews video footage.

• Tags and returns the right clip.
  

No manual labelling. The agent figured it out.

Simple UI. Complex backend. Zero human decision-making is required.

Summary: The 3 Levels

If you're still reading, congrats! You officially know more about AI agents than 90% of the self-proclaimed LinkedIn experts.

More mind-expanding breakdowns are on the way. 

Till then, stay curious. (because that's what makes you human)

— Written by Aaron & The Clever Nest Team