Demystifying AI Agents: From Simple LLMs to Autonomous Assistants
I recently watched this super helpful video that breaks down AI agents in a way that actually makes sense to me. As someone who uses AI tools but doesn't have a tech background, I appreciated how it laid everything out in three simple levels.
At the most basic level, we've got Large Language Models (LLMs) - these are the engines behind Chat GPT, Claude, and Google Gemini that we're all using. They're pretty good at generating text when you prompt them, like asking for email drafts. But here's the thing - they've got serious limitations. They don't know what's in your calendar or have access to your personal stuff unless specifically connected. They just sit there waiting for you to ask something. Ever notice that?
The second level gets more interesting with AI workflows. I've found these fascinating because they follow preset steps using external tools. Like, if you ask about your coffee meeting with someone, the workflow might check your Google calendar first, then respond. But if you suddenly ask about the weather that day? Nothing. It can't pivot unless that specific path was programmed in. This is where Retrieval Augmented Generation (RAG) comes in - it lets the AI grab info from outside sources before answering.
You know what's cool? I could actually build something like this myself using make.com - a workflow that grabs news articles, summarizes them with Perplexity, drafts social posts with Claude, and schedules everything for 8 AM daily. No coding required!
The third level is where things get wild - actual AI agents. These don't just follow steps; they reason through problems, use tools on their own, and keep improving their results. For example, Andrew Ng demonstrated an AI Vision Agent that can hunt through videos for "skiers" by figuring out what skiers look like and finding the right clips - all on its own! This uses something called the ReAct framework, where the AI both reasons AND acts independently.
What's the real difference between all these levels? Autonomy. Can the system make decisions, or is it just following my instructions? An agentic assistant can critique its own work and improve it without me babysitting every step.
Despite all this complex tech happening behind the scenes, the tools themselves are getting simpler to use. That's what matters for most of us, right?
Have you tried building your own AI workflow yet? Or maybe you're curious about how agentic automation might help with specific tasks? I'd love to hear about it!