You know, it feels like just yesterday when ChatGPT dropped, and we were all blown away by a machine that could write poetry. But if you’re actually building with AI right now, you’ve probably noticed something huge shifting. We aren't just building "chatbots" anymore. We’re building Agents.
And honestly? It’s a completely different ballgame.
The Difference: Think "Calculator" vs. "Intern"
Here’s the simplest way I can put it:
A standard LLM (like basic ChatGPT) is like a really smart encyclopedia or a calculator. You ask it a question, and it gives you an answer based on what it knows. If it doesn't know, it hallucinates or just shrugs. It's passive.
An Agentic AI, on the other hand, is like hiring a smart intern.
If you tell an agent, "Find me the latest research on Quantum Computing and summarize it," it doesn't just guess. It goes:
- "Okay, I need to search the web."
- "I found 5 papers, let me read the abstracts."
- "This one looks irrelevant, I'll skip it."
- "I'll summarize the rest and write a report."
It has agency. It has tools (like Google Search, Python, or a Database), and it has a "brain" (the LLM) to decide when to use them.
The Magic Loop: Reason and Act
Under the hood, this is often called the ReAct pattern (Reason + Act). It sounds fancy, but it's really just a loop that looks like this:
- Thought: The AI looks at your request. "User wants to check the weather."
- Plan: "I don't know the weather, but I have a weather API tool. I should use that."
- Action: It actually executes code.
get_weather("New York"). - Observation: The code returns
"15°C, Cloudy". - Synthesis: The AI looks at that data and says, "Okay, now I can answer the user."
When you see it running in the terminal, it feels like magic. You see the AI "thinking" in real-time, making mistakes, correcting itself, and pushing forward. It’s messy, sure, but it feels... alive.
Why It's Actually Hard
Building these things isn't just about chaining prompt calls. I learned this the hard way while building my Deep Research Agent.
The biggest challenge is reliability. Imagine if your intern got distracted every 5 minutes or forgot what they were doing halfway through a task. That’s what happens with Agents if you don't manage their "context" (memory) properly. They can get stuck in loops, obsess over the wrong details, or just hallucinate a tool that doesn't exist.
To make them robust, we have to use frameworks like LangChain or LangGraph. We have to essentially "program" their cognitive architecture—defining how they should react when they hit a wall.
Where We Go From Here
I really believe we are moving towards a future where software isn't just a set of buttons you click. It's going to be a set of goals you define.
Instead of clicking "Book Flight" -> "Select Date" -> "Enter Card," you'll just say "Book me a flight to London for that conference next week," and your personal agent will talk to the airline's agent, negotiate the price, and handle it.
It’s exciting, it’s a little scary, but mostly? It’s just really cool to be a part of it.
P.S. If you're curious about the code behind my agents, check out the Projects page. I've open-sourced the frameworks I use!