Learn About AI Agents in a Few Minutes: The Essential Guide

1. What Is an AI Agent?

One of the trickiest parts of studying AI agents is finding a universally accepted definition. The concept is new, and there’s still no total consensus on how it “should” work or what it “really” is.

The easiest way to start is to look at what an AI agent is not. For example, doing a “one-shot prompt”—such as asking ChatGPT directly:

“Write me a complete essay on a given topic.”
This can work, but you often end up with a response that’s a bit too vague or not detailed enough.

Agentic approaches, on the other hand, break the problem into multiple steps, involve reviewing, additional research, and refining the answer over time. This is a more “circular” workflow rather than “linear.” The agent (or the user) will think, research, propose a draft, revise, and repeat until the final result is reached.

There’s also the idea of a fully autonomous agent, capable of independently deciding which steps to take, which tools to use, and how many rounds of revision to apply. As of now, we mostly see agents hovering between “semi-autonomous” workflows and one-and-done prompting. But given how fast AI progresses, who knows—maybe in a couple of months we’ll have fully autonomous agents!

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2. Four AI Agent Design Patterns

There are four main “design patterns” for organizing AI agents:

1. Reflection
   You instruct the AI to review and improve its own output. For instance, the AI generates a piece of code, and then you prompt it to analyze and correct any errors line by line.
2. Tool Use
   You provide the AI with tools (web search, code execution, etc.) so it can gather external information or perform specific tasks. Rather than simply guessing an answer, it can actually research and analyze data in real time.
3. Planning and Reasoning
   The AI can plan and think through the sequence of steps needed to solve a complex problem. For example, creating an image from another image, then describing it in audio, requires multiple models that the AI must coordinate.
4. Multi-Agents
   Instead of using a single language model for everything, you distribute tasks to multiple specialized AIs—like having a “writer” and an “editor.” This team approach, inspired by human collaboration, often yields more precise, richer outcomes.

3. Real-World Examples

• Analyzing a video: An AI agent can split a video into segments, detect a specific moment (e.g., a goal scored at 5 seconds), and isolate that moment.
• Research Assistant: An agent that searches for information online, synthesizes it, and produces a thorough report.
• Collaborative Coding: A “Writer” agent creates code while a “Reviewer” agent corrects and optimizes it.

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4. Multi-Agent Architectures: A Quick Overview

You can organize multiple AI agents in various ways:

• Sequential: Each agent works one after another, like an assembly line.
• Hierarchical: A “manager” AI oversees several specialized “sub-agents.”
• Hybrid: Combines sequential and hierarchical structures with continuous feedback loops.
• Parallel: Multiple agents work simultaneously on different parts of a task.
• Asynchronous: Agents run independently at different times, triggered by events (e.g., flagging suspicious network activity).

The more complex the system, the more it resembles a large company—complete with numerous interactions, processes, and a bit of creative chaos!

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5. Creating an AI Agent Without Coding: A Practical Example

Even if you’re not a fan of coding, it’s now possible to build a sophisticated AI agent using tools like n8n or Make.com. For instance, I built a Telegram assistant that:

1. Receives my text or voice messages.
2. Converts voice to text if needed.
3. Checks my Google Calendar to see my tasks for the day.
4. Helps prioritize those tasks and can even create calendar events.

This is done through a workflow connecting various “nodes”:

• A Telegram trigger (receives your message),
• The AI component (OpenAI GPT-4, Claude, etc.),
• Google Calendar access,
• Communication back to the user, etc.

With just one agent plus a few tools, you can already achieve something quite powerful. Imagine what you could do with an entire team of agents, each with its own specialty—working together to optimize schedules, draft reports, or manage customer service!

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6. Opportunities to Build Your Own AI Business

The most interesting part for entrepreneurs and developers comes from a key tip in a Y Combinator talk:

For every existing SaaS (Software as a Service) company,
there will be an AI agent version.

In other words, if you’re looking for an AI-based business idea, simply take a successful SaaS model and adapt it into an AI agent. Examples might include creative design platforms, e-commerce solutions, project management, etc. There are countless possibilities, so jump in!

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7. A Quick Assessment

If you’ve read this far, great job! Here are a few questions to test what you’ve learned:

1. What’s the difference between a non-agentic workflow and an agentic one?
2. Name at least two agentic design patterns.
3. Give an example of a tool an AI agent might use.
4. Why consider a multi-agent approach rather than a single agent?

If you can answer them all, congratulations—you’ve got a solid grasp of AI agent basics!

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AI agents are an exciting, fast-evolving field. They’re iterative, powerful, and open doors for endless new applications. With the right patterns, tools, and a basic understanding of multi-agents, you’re ready to explore a fertile new area—whether for personal projects or business ventures. Ready to take action?

Want to go even further?
Head over to sundatalab.io to find resources, tools, and personalized support to help you level up your data and AI projects!

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