AI agents pop up everywhere these days. Terms like agentic capabilities and agentic workflows sound scary, but they’re not. If you use tools like ChatGPT every day and want to grasp how AI agents might change your routine, this guide fits you. We’ll break it down in three simple steps. Start with what you know, like chatbots. Then add AI workflows. Finally, reach AI agents. Along the way, we’ll simplify buzzwords like RAG and ReAct. No tech degree needed. Just real-life examples to show the impact.
Table of Contents
Level 1: The Foundation – Large Language Models (LLMs)
Large language models power chatbots you love. Think ChatGPT, Google Gemini, or Claude. These tools shine at creating or tweaking text. They draw from huge piles of training data to spit out responses.
You type a question. The model thinks fast and replies. It’s like a smart friend who recalls facts from books. But it has limits. We’ll cover those soon.
Input and Output: The Basic LLM Interaction
Picture this. You need a polite email for a coffee chat. You tell ChatGPT your request. That’s the input, your prompt. Out comes a draft, smoother than anything you’d write yourself.
The flow stays simple. Input goes in. Output comes out. No extra steps. This setup works great for quick tasks like brainstorming ideas or fixing sentences.
What happens if you push it further? Say, ask about your schedule. The model guesses or says it can’t help. Why? It lacks your personal details.
Key Traits of Base LLMs: Passivity and Data Gaps
LLMs sit and wait. They react only when you prompt them. No initiative. They’re passive tools, like a calculator that needs you to press buttons.
Their knowledge stops at training data. No access to your calendar or company files. That’s a big gap. Personal info stays private, which is good, but it blocks real help.
These traits matter. They explain why basic chatbots falter on custom needs. Keep this in mind as we build up. Next, we connect LLMs to fix some issues.
Level 2: Automating Paths – Understanding AI Workflows
Now, take that basic LLM and add smarts. AI workflows link it to outside sources. You set the rules, like a recipe for the AI to follow.
Humans design every turn. It’s control logic, plain and simple. The AI sticks to your plan. No surprises.
This step solves data problems. But it can’t adapt on its own. Let’s see how.
Introducing External Data: The Calendar Example
Remember the coffee chat email? Easy for ChatGPT. But ask, “When’s my next one?” It fails. No calendar access.
Fix that with a workflow. Tell the LLM: Before answering personal events, check my Google Calendar. Now, ask about your chat with a friend. It pulls the date first. Success.
Follow up: “What’s the weather then?” Boom, it stumbles. Your path only covers calendars. Weather needs another tool. Workflows lock into what you define.
You see the power. And the catch. Humans map the route. AI just travels it.
The Role of Retrieval Augmented Generation (RAG)
RAG sounds fancy. But it’s straightforward. It lets AI grab fresh info before replying. Like pulling calendar data or weather updates.
This fits as an AI workflow type. You set it up to search specific spots. No more blind guesses.
In practice, RAG boosts accuracy. Say you’re drafting reports. It fetches company stats. Your outputs get sharper. Simple tweak, big win.
Tools like APIs make it happen. Connect weather services or databases. RAG turns passive AI active, but still on your terms.
Real-World Workflow Example: Multi-Step Automation (Make.com)
Want a daily social media boost? I built one using Make.com. It pulls news links into Google Sheets. That’s step one.
Next, Perplexity summarizes the articles. Short and sweet. Then Claude crafts posts for LinkedIn and Instagram. I tweak the prompt for my style.
Finally, schedule it for 8 a.m. daily. Run and forget. This workflow hums without me hovering.
But test it. The LinkedIn post might fall flat. Not funny enough? I rewrite the prompt by hand. Trial and error stays human work.
- Step 1: Gather links in Sheets.
- Step 2: Summarize with Perplexity.
- Step 3: Draft posts via Claude.
- Step 4: Auto-post every morning.
This setup saves hours. Yet, it’s no agent. I call the shots.
Level 3: The Autonomous Shift – Defining AI Agents
Here’s the leap. Swap the human boss for the LLM itself. Now you have an AI agent. It thinks and moves on its own.
The goal? Reach the end without your tweaks. Agents handle reasoning and action. They iterate too.
This changes everything. From rigid paths to smart choices.
The Core Mandate: Reasoning and Acting
Give an agent a goal, like “Make social posts from news.” It reasons first. What’s the best way? Links in Sheets beat copying text. Faster fetch.
Then it acts. Picks tools that fit. Google Sheets wins over Word. Your account’s linked already.
ReAct sums it up. Reason, then Act. Repeat. Most agents use this setup.
Why Sheets? Efficiency. Agents weigh options like you would. But quicker.
In my news example, the agent skips bad paths. No pasting into docs. Straight to smart tools.
Autonomous Iteration and Self-Correction
Back to that dull post. In a workflow, I fix it manually. Agents don’t need me.
It drafts version one. Then critiques itself. “Add humor. Check best practices.” Another LLM reviews.
Loop until it shines. Meets the goal? Done. No human loops.
This self-fix magic saves time. Imagine emails that polish themselves. Or reports that refine alone.
Agents learn from each step. They observe results. Adjust on the fly.
Real-World Agent Demonstration: Vision and Indexing
Andrew Ng shows this well. His demo site searches video clips. Type “skier.” The agent kicks in.
First, it reasons. A skier? Person on skis, speeding through snow. Clear image.
Then acts. Scans footage. Spots matches. Indexes the clip with tags like “snow” or “mountain.”
Returns the video. All auto. No human sifting hours of tape.
This feels basic. But think: Agents tag media solo. Saves teams tons of work.
The backend’s complex. Front end? Simple search bar. Users like us get results without the hassle.
I’m tinkering with one using n8n. Basic agent for tasks. What should I build next? Share ideas below.
Conclusion: Three Levels of AI Capability Comparison
We started simple. Now you see the progression. LLMs respond. Workflows add paths. Agents decide and adapt.
Each level builds value. Pick what fits your needs. Start with workflows for automation. Push to agents for freedom.
Key takeaways:
- LLM (Level 1): You input a prompt. AI outputs text. It’s passive and sticks to trained knowledge.
- Workflow (Level 2): Input plus a set path. Pulls external data like calendars. Humans design every move, including fixes.
- AI Agent (Level 3): Give a goal. Agent reasons paths, acts with tools, observes, and iterates. LLM leads the show.
This shift to agentic workflows means less hand-holding. Your AI gets smarter daily. Try building a simple workflow today. Tools like Make.com make it easy.
For more, check my guide on prompt databases in Notion. Grab my free AI toolkit too. Links below. What’s your first agent idea? Drop a comment.
