What are AI Agents? How Can We Create Our Own AI Agents for Free ?
What are AI Agents- I learned about AI agents just to explore how it works. There’s not actually one course that just fully nicely covers everything. So I did three courses, wrote a bunch of papers, and watched a lot of YouTube videos as well. And ofcourse actually made my own agents too.
First we’re going to talk about what even are AI agents. It is such a hyped up term now. Then we’ll learn specifically multi-agent architectures — it’s a really interesting developing field.
I will tell you how to create an AI agent workflow which does not require any code. I was honestly so shocked by how powerful and easy to use as well these workflows are.
Then finally, for those of you who are interested in getting into the field or even building your own AI agents for your businesses, I will give you ideas to create your won AI Agents.
What Are AI Agents?
Okay so let’s first define agents. Believe it or not one of the most difficult things from this entire deep dive into AI agents for me was just the actual definition of an AI agent. It is probably because it is such a new field and people are still trying to figure out what even it is and like how it works.
The AI Agents is a type of articficial intelligence that can understand and respond to customer queries. These agents does not require human intervention. And they work on the autonomous system.
You should know what are AI Agents and What are not an AI Agents.
What Is Not an AI Agent? (One-Hot Prompting)
The easiest way to first define AI agents is to give an example of what is not an AI agent. What is definitely not an AI agent is if you just ask an AI to do something for you, otherwise known as one-hot prompting.
For example,
If you just go to ChatGPT and write:
“Please write out an essay on topic X from start to finish in one go.”
You’ll still get a response and it’ll still be coherent and on topic, but it’ll probably also be quite vague and probably not what you were looking for.
Agentic Workflow vs Non-Agentic Workflow
Here is the difference between agentic and non-agentic workflow.
1. Non-Agentic
A non-agentic workflow is:
Start → Finish → Done.
2. Agentic
An agentic workflow is:
Think → Research → Output → Review → Improve → Repeat.
You break the task into steps:
– Outline
– Research
– Draft
– Revise
– Final output
It is circular and iterative.
Autonomous AI Agents
Now let’s add in a bit more complexity.
- Non-agentic workflow
- Agentic workflow
- Truly autonomous AI agent
A truly autonomous AI agent is when an AI can independently figure out steps, tools, revisions, and iterate until the final output.
We are not fully there yet, but progress is extremely fast.
Agentic Design Patterns
There are four massively accepted agentic design patterns:
- Reflection
- Tool Use
- Planning and Reasoning
- Multi-Agent Systems
Reflection, Tool-use, Planning, Multi-agent.
1. Reflection
You ask an AI to check and improve its own output.
Example:
Write code → Ask AI to review it for mistakes → AI fixes line 5 → Improves style and efficiency.
You can even create another AI to critique the first AI.
2. Tool Use
By giving AI access to tools, you upgrade its ability.
Examples:
– Web search
– Code execution
– Object detection
– Email/calendar access
This produces much better results than asking directly.
3. Planning & Reasoning
AI figures out the steps and tools required.
Example:
“Generate an image of a girl reading a book in same pose as the boy in example.jpg, then describe the image with your voice.”
The AI may:
– Detect pose
– Convert pose to new image
– Convert image to text
– Use text-to-speech to narrate
4. Multi-Agent Systems
Instead of one LLM doing everything, multiple AIs take specialized roles.
Like humans in a company — specialization improves results.
Research shows multi-agent workflows outperform single-agent approaches.
Real Examples of AI Agents
– Counting players in sports images
– Splitting videos and identifying goals
– AI-powered research assistants
– AI writers
– AI coders
– AI personal assistants
All rely on agentic patterns.
Prompt Engineering Still Matters
Prompt engineering is still a major factor in agentic success. You can learn prompt engineering from various sources. There are many courses and tutorial available online for free.
Crash Course: Multi-Agent Design Patterns
The CrewAI course (with DeepLearningAI) provides great foundations.
Building Blocks of an AI Agent
A single AI agent has 4 components:
Task – Answer – Model – Tools
Mnemonic: Tired Alpacas Mix Tea
Example: Travel Planner AI.
Simple Multi-Agent Structure
Example:
Writer agent + Editor agent.
They work together, have separate rules, separate tools.
Common Multi-Agent Patterns
1. Sequential
One agent passes to another like an assembly line.
2. Hierarchical
Manager agent + sub-agents.
3. Hybrid
Combination of sequential + hierarchical.
4. Parallel
Agents work simultaneously on different parts.
5. Asynchronous
Agents operate independently at different times — useful for security systems.
Complex Flows
These systems can be interconnected to form “flows.”
More agents = more complexity = more chaos.
Very similar to human companies.
No-Code Multi-Agent System (n8n)
You can build all of this without coding — using n8n.
Better than Make.com for multi-agent workflows.
Example project:
Telegram-based personal task assistant “Inky Bot.”
It:
– Reads your message (text or voice)
– Accesses Google Calendar
– Prioritizes your tasks
– Creates calendar events
How the Flow Works
- Telegram trigger
- Switch for text/voice
- Voice → File → OpenAI transcription
- Send text to AI agent
- AI interacts with calendar
- Sends back prioritized tasks
Conclusion-
I hope you understand what are the AI Agents. You can create your own AI Agents with the tools like n8n. These are very useful tools for automation. You can use these tools for yourself or build a business and sell these AI Agents to clients. The AI Agents are the Digital Workers that never get tired and can work all the time. You do not need to hire people to look for the work.
If you have any query then comment.