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What is Agentic AI?

Discover what makes AI agents different from traditional LLMs and why they represent the next evolution in AI

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Introduction

Core Concepts

Interactive Demo

Practical Application

Key Takeaways

Introduction

Welcome to the future of artificial intelligence, where AI systems don't just respond—they act, reason, and achieve goals autonomously.

In 2024, we witnessed a fundamental shift in AI capabilities. While traditional Large Language Models (LLMs) like GPT-4 excel at understanding and generating text, they operate in a purely reactive mode: you ask, they answer. Agentic AI represents a paradigm shift—systems that can plan multi-step workflows, use tools, remember context, and work towards objectives with minimal human intervention.

🤖What Makes an AI "Agentic"?

Autonomy: Can pursue goals with minimal human guidance

Planning: Breaks down complex tasks into actionable steps

Tool Use: Leverages external APIs, databases, and functions

Memory: Maintains context across interactions and learns from experience

Reasoning: Evaluates options, handles errors, and adapts strategies

The Evolution Journey

To understand agentic AI, let's trace the evolution of AI capabilities:

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2020-2022: Basic LLMs

Simple text completion. No memory, no tools, purely reactive.

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2023: Tool-Using LLMs

Function calling emerges. AI can now use calculators, APIs, databases.

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2024+: Agentic AI

Autonomous systems that plan, act, remember, and achieve complex goals.

�Real-World Example

Imagine you ask: "Research the top 5 AI companies, analyze their Q4 earnings, and create a comparison spreadsheet."

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Traditional LLM: Gives you generic information about AI companies based on training data (possibly outdated)

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Agentic AI: (1) Searches current financial data, (2) Extracts Q4 earnings, (3) Analyzes trends, (4) Creates spreadsheet, (5) Validates accuracy, (6) Presents results

Why Now? The Perfect Storm

Several technological advances converged in 2023-2024 to make agentic AI practical:

→Larger Context Windows: GPT-4 Turbo (128K tokens) and Claude 3 (200K tokens) can hold entire codebases in memory
→Function Calling: OpenAI and Anthropic added structured tool use, enabling reliable API integration
→Reasoning Models: o1 and similar models can "think" through problems step-by-step
→Vector Databases: Pinecone, Weaviate enable semantic memory at scale
→Framework Maturity: LangChain, AutoGen, CrewAI simplify agent development

🎯 What You'll Learn in This Module

By the end of this journey, you'll understand the core principles of agentic AI, see it in action through interactive demos, explore real-world applications, and gain hands-on experience building your own agent. Whether you're a developer, product manager, or AI enthusiast, this foundational knowledge will prepare you for the agentic AI revolution.