Everyone is talking about AI Agents.
But before you build an AI Agent, there is one concept you absolutely need to understand:
Generative AI.
Generative AI is the technology that transformed software from systems that simply follow rules into systems that can understand language, generate responses, reason through instructions, and assist users in a natural way.
As part of my new course:
Develop Your First AI Agent with Microsoft Foundry
I published the first lesson where we explore the journey from traditional software to Generative AI and understand why modern AI Agents became possible.
π₯ Watch the video here:
Why This Topic Matters
Many developers jump directly into AI Agents, prompts, tools, and frameworks.
However, without understanding the evolution of AI, it becomes difficult to understand:
- Why AI Agents exist
- Why Large Language Models are important
- Why prompts work
- Why tools are needed
- How modern AI systems actually operate
In this lesson, we start from first principles and build the foundation required for the rest of the course.
What You'll Learn
Traditional Software
For decades, software followed a simple pattern:
Input β Rules β Output
Developers explicitly defined every behavior.
This worked well until humans started interacting with software using natural language.
Why Rule-Based Systems Break
Imagine building a dietician chatbot.
Users might ask:
- What should I eat?
- Suggest a healthy breakfast.
- What foods contain protein?
- Can I eat oats daily?
All of these questions are similar.
Yet they are phrased differently.
Supporting thousands of variations quickly becomes impossible with manually written rules.
Predictive AI
Machine Learning introduced a new approach.
Instead of writing rules, we train models using data.
Examples include:
- Spam Detection
- Fraud Detection
- Recommendation Systems
Predictive AI can make decisions.
But it still cannot create content.
Prediction vs Creation
A predictive model can answer:
Fraud probability: 87%
But can it explain why?
Can it write a detailed report?
Can it create a personalized recommendation?
Not naturally.
This limitation led to the rise of Generative AI.
Generative AI
Generative AI creates new content.
It can generate:
- Text
- Images
- Audio
- Video
- Code
Instead of selecting predefined responses, it dynamically creates new outputs based on user prompts.
Large Language Models (LLMs)
At the heart of Generative AI are Large Language Models.
LLMs learn language patterns from enormous amounts of data and use those patterns to generate human-like responses.
This is the technology behind modern AI systems such as ChatGPT, Microsoft Copilot, Gemini, Claude, and many others.
The Generative AI Flow
Every Generative AI application follows a simple architecture:
User Prompt β LLM β Generated Response
Understanding this flow is critical because it becomes the foundation of AI Agent architectures.
AI Agent Architecture
In the second half of the course, we will build an AI Agent using Microsoft Foundry.
The architecture we'll implement is:
User
β
React + Vite Frontend
β
Microsoft Foundry Agent
βββ Instructions
βββ Generative AI Model
βββ Web Search Tool
β
Response
Understanding Generative AI is the first step toward understanding this architecture.
Introducing Subra AI Dietician
Throughout the course, we will build:
Subra AI Dietician
A practical AI-powered dietician assistant that can:
- Answer nutrition questions
- Provide healthy food suggestions
- Follow custom instructions
- Use web search when required
- Respond through a modern web interface
By the end of the course, you'll have a complete working AI Agent built using Microsoft Foundry.
Video Chapters
00:00 Introduction
01:30 The Problem With Traditional Software
02:30 Why Rule-Based Systems Break
03:51 The Rise of Predictive AI
05:06 Prediction vs Creation
05:55 What is Generative AI
06:43 What is LLM?
07:36 The Generative AI Flow
08:18 AI Agent Architecture
09:06 Subra AI Dietician
What's Coming Next?
In the next lesson, we'll answer a very important question:
If Generative AI can already answer questions, why do we need AI Agents?
We'll explore:
- AI Assistant vs AI Agent
- Instructions
- Tools
- Reasoning
- Agent Workflows
and prepare for building our first Microsoft Foundry Agent.
If you're interested in AI Engineering, Microsoft Foundry, Azure AI, Agentic AI, or building practical AI applications, this series is designed for you.
Happy learning!
β Subrata Kumar Das
π subraatakumar.com
