{"slug": "what-is-generative-ai-understanding-the-foundation-of-modern-ai-agents-2", "title": "What is Generative AI? Understanding the Foundation of Modern AI Agents #2", "summary": "Subrata Kumar Das published a lesson on Generative AI as part of a new course, explaining the evolution from traditional rule-based software to modern AI agents. The lesson covers the shift from predictive AI to generative AI, the role of large language models, and the architecture of AI agents, with a practical example using Microsoft Foundry to build a dietician assistant.", "body_md": "Everyone is talking about AI Agents.\n\nBut before you build an AI Agent, there is one concept you absolutely need to understand:\n\n**Generative AI.**\n\nGenerative 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.\n\nAs part of my new course:\n\nI published the first lesson where we explore the journey from traditional software to Generative AI and understand why modern AI Agents became possible.\n\n🎥 **Watch the video here:**\n\nMany developers jump directly into AI Agents, prompts, tools, and frameworks.\n\nHowever, without understanding the evolution of AI, it becomes difficult to understand:\n\nIn this lesson, we start from first principles and build the foundation required for the rest of the course.\n\nFor decades, software followed a simple pattern:\n\nInput → Rules → Output\n\nDevelopers explicitly defined every behavior.\n\nThis worked well until humans started interacting with software using natural language.\n\nImagine building a dietician chatbot.\n\nUsers might ask:\n\nAll of these questions are similar.\n\nYet they are phrased differently.\n\nSupporting thousands of variations quickly becomes impossible with manually written rules.\n\nMachine Learning introduced a new approach.\n\nInstead of writing rules, we train models using data.\n\nExamples include:\n\nPredictive AI can make decisions.\n\nBut it still cannot create content.\n\nA predictive model can answer:\n\nFraud probability: 87%\n\nBut can it explain why?\n\nCan it write a detailed report?\n\nCan it create a personalized recommendation?\n\nNot naturally.\n\nThis limitation led to the rise of Generative AI.\n\nGenerative AI creates new content.\n\nIt can generate:\n\nInstead of selecting predefined responses, it dynamically creates new outputs based on user prompts.\n\nAt the heart of Generative AI are Large Language Models.\n\nLLMs learn language patterns from enormous amounts of data and use those patterns to generate human-like responses.\n\nThis is the technology behind modern AI systems such as ChatGPT, Microsoft Copilot, Gemini, Claude, and many others.\n\nEvery Generative AI application follows a simple architecture:\n\nUser Prompt → LLM → Generated Response\n\nUnderstanding this flow is critical because it becomes the foundation of AI Agent architectures.\n\nIn the second half of the course, we will build an AI Agent using Microsoft Foundry.\n\nThe architecture we'll implement is:\n\nUser\n\n↓\n\nReact + Vite Frontend\n\n↓\n\nMicrosoft Foundry Agent\n\n├── Instructions\n\n├── Generative AI Model\n\n└── Web Search Tool\n\n↓\n\nResponse\n\nUnderstanding Generative AI is the first step toward understanding this architecture.\n\nThroughout the course, we will build:\n\nA practical AI-powered dietician assistant that can:\n\nBy the end of the course, you'll have a complete working AI Agent built using Microsoft Foundry.\n\n00:00 Introduction\n\n01:30 The Problem With Traditional Software\n\n02:30 Why Rule-Based Systems Break\n\n03:51 The Rise of Predictive AI\n\n05:06 Prediction vs Creation\n\n05:55 What is Generative AI\n\n06:43 What is LLM?\n\n07:36 The Generative AI Flow\n\n08:18 AI Agent Architecture\n\n09:06 Subra AI Dietician\n\nIn the next lesson, we'll answer a very important question:\n\nIf Generative AI can already answer questions, why do we need AI Agents?\n\nWe'll explore:\n\nand prepare for building our first Microsoft Foundry Agent.\n\nIf you're interested in AI Engineering, Microsoft Foundry, Azure AI, Agentic AI, or building practical AI applications, this series is designed for you.\n\nHappy learning!\n\n— Subrata Kumar Das\n\n🌐 subraatakumar.com", "url": "https://wpnews.pro/news/what-is-generative-ai-understanding-the-foundation-of-modern-ai-agents-2", "canonical_source": "https://dev.to/subraatakumar/what-is-generative-ai-understanding-the-foundation-of-modern-ai-agents-2-3jgn", "published_at": "2026-06-19 06:20:04+00:00", "updated_at": "2026-06-19 06:30:07.480726+00:00", "lang": "en", "topics": ["generative-ai", "large-language-models", "ai-agents", "developer-tools"], "entities": ["Subrata Kumar Das", "Microsoft Foundry", "ChatGPT", "Microsoft Copilot", "Gemini", "Claude", "Azure AI", "Subra AI Dietician"], "alternates": {"html": "https://wpnews.pro/news/what-is-generative-ai-understanding-the-foundation-of-modern-ai-agents-2", "markdown": "https://wpnews.pro/news/what-is-generative-ai-understanding-the-foundation-of-modern-ai-agents-2.md", "text": "https://wpnews.pro/news/what-is-generative-ai-understanding-the-foundation-of-modern-ai-agents-2.txt", "jsonld": 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