We are learning about AI Agents. In today’s newsletter, we'll break down what is AI agents are, how they work, and how it is different from simple automation.

In today’s edition:

• AI Deep Dive— AI Agents 101

• Build Together— Here’s How I Can Help You

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[AI Deep Dive]

AI Agents 101 - Everything You Need To Know About Agents

Imagine if your smart fridge had an AI agent.

It wouldn't just order milk when you're out.

It would notice you've been browsing vegan recipes, check your calendar for that upcoming health check-up, and then debate with itself whether almond milk is a better choice for your new lifestyle.

Scary or helpful?

You decide!

This is the simplest way I can define an AI agent:

They can handle complex, multi-step challenges, making them far more dynamic than the basic automation tools or chatbots we're used to.

They are complete software systems, not just scripts, designed for complex interactions with their environment.

How Are AI Agents Different from a Chatbot?

You must be asking this exact question.

The difference comes down to two powerful capabilities that chatbots lack:

• Tools

• Planning

You’ve seen ChatGPT make mistakes in basic math.

That’s because it's a thinker, not a doer. (Now, because of the latest updates, it can do what agents do, but you got the point)

It can only respond based on the text patterns it was trained on.

Now, think about how you would solve a math problem like 85 x 65.

If you're a math whiz, you might know the answer.

But most of us would reach for a tool, which is a calculator.

You know what to do (multiply), and you know how to use the tool to get it done.

This is exactly what makes an AI agent different.

You are giving the AI access to tools.

But just having a calculator isn't enough.

You also need a plan.

You need to know that you should input 85, then the multiply (X) symbol, then 65.

That simple sequence is a plan born from reasoning.

That’s the magic of an AI agent.

It doesn't just talk, it thinks, plans, and acts.

The Architecture of an AI Agent

So, what happens when you give an agent a task?

The process flows through three core components.

Think of it as a small company with a manager, a brilliant strategist, and a team of specialists.

The 3 major components are:

1. Orchestration Layer (The Control Center / Manager)

2. Model (The Brain / Strategist)

3. Tools (The Hands / Specialists)

Let's break down each one.

1) Orchestration Layer [The Control Center]

Let’s say I want to create an AI meeting scheduler. I give it a goal:

Host a webinar for all my students.

This query is the trigger.

It doesn't go straight to the AI model, it goes to the Orchestration Layer, the agent's control center.

This layer is the manager responsible for the entire operation. It has four main jobs:

• Memory - remembering the context of our conversation so it knows who my students are

• State - keeping track of the current status. Has the email been drafted? Is it waiting for a response?

• Reasoning - guiding the overall logic. “To host a webinar, I first need a list of students, then I need to check everyone’s availability, then I need to book a time.”

• Planning - breaking the goal down into concrete, actionable steps and deciding what to do next.

The Orchestrator decides what needs to happen and then consults with the brain.

2) Model [The Brain]

The model is the centralized decision-maker, the brain of the operation.

This is typically a powerful Large Language Model (LLM).

To understand the query, formulate a plan, and determine the next action, the model uses advanced reasoning frameworks like:

ReAct (Reason + Act)

A framework that forces the model to "think out loud" about why it’s taking an action before it does it.

This ensures deliberate, logical steps.

Chain-of-Thought

Breaking a complex problem down into a series of intermediate steps, just like you would when solving a problem on paper.

Tree-of-Thoughts

Exploring multiple reasoning paths simultaneously to find the most promising solution, instead of just following one line of thought.

After thinking, the model determines what specific action to take and which tool to use.

3) Tools [The Hands]

The agent interacts with the outside world using its Tools.

These are the hands of the operation, executing the tasks that the brain plans.

Just like you use a calculator, an agent can use APIs, search engines, or databases.

Tools enable agents to perform actions far beyond the model's built-in capabilities. There are three main types:

Extensions

Live API calls to external services. (connecting to the Google Calendar API to check for open slots).

Functions

Code that the agent can execute locally. (running a Python function to calculate time zone differences for the students).

Data Stores

Access to knowledge bases, like vector databases (using RAG), or traditional databases to pull a list of students.

The model decides which tool to use (google_calendar.find_available_slot) and what information to give it ( {attendees: [student_list], duration: '60mins'} ).

The tool then executes the action in the real world.

This whole process, Orchestrate, Think, Act, repeats in a loop until the final goal is achieved.

Your webinar is now successfully scheduled, all from a single request.

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