Understanding AI Agents: How They Work and Why They Matter

AI agents are reshaping productivity by moving beyond simple prompts and workflows. This guide walks you through the evolution from large language models (LLMs) to fully autonomous AI agents, packed with real-world examples and easy-to-follow explanations.

Overview

Artificial Intelligence is moving fast, and terms like “agentic workflows” and “AI agents” are dominating the conversation. But what do they actually mean? If you’re someone with little to no technical background who uses AI tools regularly, this guide breaks down AI agents in a simple, practical way. By exploring real-world examples and relatable contexts, we uncover exactly how AI agents differ from AI workflows and why it’s important for everyday users to understand the shift.

Level 1: Large Language Models (LLMs)

Large Language Models (LLMs) are the foundational technology behind popular tools like ChatGPT, Google Gemini, and Claude. These models are capable of generating and editing text based on the input provided to them by users.

Here’s the basic model:

  • Input: You, the human, provide a prompt.
  • Output: The LLM analyzes this and produces a response.

Example: You ask ChatGPT to write a polite email requesting a meeting. The AI crafts a more professional and courteous message than you might write yourself.

However, LLMs have limitations:

  1. Lack of proprietary knowledge: They don’t have access to your personal data (e.g. your calendar).
  2. Passive behavior: They operate only when prompted and don’t take any action themselves.

Level 2: AI Workflows

AI workflows involve chaining steps together to build more reactive systems. Unlike LLMs, which generate text, workflows can integrate external data and trigger specific actions — but only based on a predefined path set by a human.

Let’s say you ask your AI assistant about your next meeting. If configured correctly, it could check your Google Calendar and return the correct details. Add another step, and it could pull the weather forecast for that day from an API. You could even use a text-to-audio model to read that forecast aloud.

The key characteristics of AI workflows include:

  • They follow predefined control logic — steps created manually by a human.
  • They can integrate with tools and APIs, but they don’t deviate from the set instructions.

Pro tip: You may hear the term RAG – Retrieval-Augmented Generation. It’s just a form of AI workflow where the AI first retrieves real-time data (like weather or calendar info) before answering.

A real-life example is using tools like make.com to automate content generation. By linking Google Sheets to collect news articles, summarizing them using Perplexity, drafting social media posts using Claude, and scheduling posts at 8 AM daily, you’ve created a powerful, repeatable AI workflow. But you, the human, still guide the path and make corrections when needed.

Level 3: AI Agents

Here’s where things get exciting. An AI agent elevates a simple workflow by turning over key decisions to the AI itself. It’s not just following steps — it’s reasoning, deciding, acting, and iterating like a person would.

The defining characteristics of AI agents are:

  1. Reasoning: The AI evaluates what must be done to achieve the goal.
  2. Action: It carries out tasks across tools or platforms autonomously.
  3. Iteration: It checks its results and makes improvements without human intervention.

Imagine replacing the human component in the earlier workflow. The new AI agent could decide the best method to collect articles, analyze them, generate social captions, and keep fine-tuning them for tone or clarity — all without your assistance.

This shift makes the AI the true “decision maker” in the loop — a huge leap from earlier models.

The most common framework for AI agents is called ReAct — short for Reason + Act. It’s about thinking before doing, and doing with awareness of the overall goal.

Another benefit of AI agents is their ability to self-assess. If version one of your LinkedIn post isn’t engaging enough, an AI agent can bring in a second LLM to critique it, improve the copy using guidelines like best practices for engagement, and repeat this loop until the content meets a satisfactory standard.

Real-World AI Agent Example

AI agents aren’t just theoretical anymore. One demonstration involved searching for a “skier” in video footage. The AI agent processed the term skier, reasoned about what it might look like, scanned through video clips, identified relevant footage, indexed it, and returned that to the user — all autonomously. No human needed to tag or manually filter the video. This showcases the tremendous decision-making capacity and autonomy agents are beginning to achieve.

Tools and demonstrations may still be early-stage, but they reveal the power of agents to reduce workloads, make decisions, and produce tailored results faster and more effectively than ever before.

Key Differences Recap

Let’s summarize the evolution from LLMs to agents:

  • LLMs: You prompt, it responds. Simple, passive text generation.
  • AI Workflows: You design a series of steps for the AI to follow. It can interact with multiple tools, but sticks to the script.
  • AI Agents: The AI takes over your decision-making role. It reasons, acts, and improves results iteratively toward a defined goal.

Conclusion

AI agents represent a significant shift in how artificial intelligence tools are used. Moving from simple prompts and workflows to dynamic agents means unlocking greater productivity, smarter automation, and more adaptable systems. Understanding these distinctions is crucial for anyone looking to stay informed, competitive, and efficient in this AI-powered era.

As AI continues to evolve, bridging the gap between humans and autonomous systems, understanding the difference between workflows and agents will empower users to build better solutions and capitalize on the full potential of artificial intelligence.

Note: This blog is written and based on a YouTube video. Orignal creator video below:

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