Unpacking AI Agents: Evolution path from LLMs to AI Workflow to AI Agent

You're likely using AI tools like ChatGPT or Gemini regularly. They're fantastic for drafting emails, brainstorming ideas, and so much more. But the AI landscape is evolving at lightning speed, and terms like "AI Agents" are popping up everywhere. If you're wondering what they are and how they differ from the AI you already know – without needing a degree in computer science – you're in the right place.

Let's break it down with a simple 1-2-3 learning path, building on concepts you're already familiar with, and see how this evolution might just change the way we work.

Level 1: Large Language Models (LLMs) – The Smart Text Generators

Think of popular AI chatbots. Under the hood, they're powered by Large Language Models (LLMs). These LLMs are trained on vast amounts of text data, making them incredibly skilled at understanding, generating, and editing human language.

You give an input (a prompt), and the LLM produces an output. For instance, you ask it to "draft a polite follow-up email for a sales lead," and it generates a well-worded message. Simple and effective.

However, LLMs have a couple of key characteristics to remember:

1. Limited Knowledge: Their knowledge is generally cut off at the time of their last training and they don't inherently know your personal information, your company's internal data, or real-time specifics unless explicitly given in the moment. Ask ChatGPT when your next meeting is, and it won't know – it can't see your calendar.

2. Passive Nature: LLMs wait for your prompt and then respond. They don't take initiative.

These traits are crucial as we move to the next level.

Level 2: AI Workflows – Connecting the Dots (With You in Charge)

Now, what if we could give an LLM a bit more guidance to access information or perform a sequence of tasks? That's where AI Workflows come in.

An AI Workflow is essentially a series of steps that you (the human) define for an LLM (or multiple AIs) to follow. You set up the "control logic" – a predefined path.

For example, you could tell an LLM: "When I ask about a personal event, first search my connected Google Calendar, then provide a response." Now, if you ask, "When is my coffee chat with Liam?" it can give you the correct answer because it followed your instructions to check the calendar.

But here's the catch: if you then ask, "What will the weather be like that day?" the workflow might fail. Why? Because the path you defined was only to search the calendar, which doesn't store weather information. AI Workflows stick rigidly to the script you provide.

You might have heard the term RAG (Retrieval Augmented Generation). In simple terms, RAG is often a type of AI workflow where the AI is instructed to "look things up" in a specific knowledge base or document before generating an answer.

Many no-code automation platforms allow you to build these workflows. For instance, you could create a workflow that:

1. Pulls links to industry news articles from a Google Sheet.

2. Sends those articles to an AI like Perplexity to summarize them.

3. Uses another AI, like Claude, with a specific prompt you wrote to draft LinkedIn posts based on those summaries.

4. Schedules these posts to run automatically.

This is powerful automation! However, the human is still the core decision-maker. You set the path, you provide the prompts, and if the output isn't quite right (e.g., the drafted LinkedIn post isn't engaging enough), you have to go back and manually tweak the prompts or the workflow steps.

Level 3: AI Agents – The AI Starts Making Decisions

This is where things take a fascinating leap. What if the AI could not only follow instructions but also decide on the best course of action and iterate on its own? Welcome to the world of AI Agents.

The single most important change from an AI Workflow to an AI Agent is this: the LLM itself becomes the decision-maker within the process.

An AI Agent is given a goal, and then it:

1. Reasons: It thinks about the best approach to achieve that goal. What steps are needed? What's the most efficient way?

2. Acts (using tools): It can select and use various tools to accomplish the steps – browse websites, access databases, run code, connect to other APIs, etc.

3. Observes & Iterates: It looks at the results of its actions and can autonomously decide if further steps or refinements are needed. If a LinkedIn post it drafted isn't good enough by certain criteria it has access to (or is given), it might try rewriting it, perhaps even by prompting another LLM to critique its work, and then try again – all without you stepping in for each micro-adjustment.

Remember our news-to-social-post workflow? An AI Agent tasked with "Create engaging daily LinkedIn posts from top industry news" might:

• Reason: "Okay, I need to find relevant news. Then I need to understand it. Then I need to write a post in a style suitable for LinkedIn. I should also make sure it's engaging."

• Act: It might decide to use a search API to find news, a web Browse tool to access articles, its own LLM capabilities to summarize and draft, and perhaps even consult a "best practices for LinkedIn" document you provided.

• Iterate: It drafts a post, then internally "critiques" it (e.g., "Is this concise? Does it have a clear call to action? Is it too dry?"). It then refines the post based on this critique, potentially several times, until it meets the desired quality.

You might hear about the ReAct framework in relation to agents. It simply stands for Reasoning and Acting – the core functions we just discussed.

This doesn't mean humans are out of the loop entirely. We still set the overarching goals, define constraints, provide necessary permissions, and, importantly, oversee the agent's performance. But the level of autonomy in how the goal is achieved is significantly higher.

6. Conclusion:

I hope this breakdown has helped demystify the journey from LLMs to the exciting potential of AI Agents! Understanding these concepts is the first step.

But theory is one thing, and practice is another. That's why in the next installment of this series, we'll roll up our sleeves and dive into a practical showcase: building a real AI Workflow from scratch. We'll see how to connect these AI "dots" to automate a tangible task.

Stay tuned for a hands-on look at bringing these powerful concepts to life!