Description

Introduction

Agentic AI represents a transformative shift in the field of Artificial Intelligence, where systems exhibit autonomy, proactivity, and goal-directed behavior. Unlike traditional AI models that rely on predefined rules or passive data processing, agentic AI systems can reason, plan, act in dynamic environments, and even collaborate with other agents or humans. This course explores the foundational principles, theoretical paradigms, and real-world applications of agentic AI, providing learners with the essential knowledge to understand and engage with this cutting-edge domain.

Prerequisites

To fully benefit from this course, participants should have:

• A basic understanding of Artificial Intelligence and Machine Learning

• Familiarity with concepts in logic, algorithms, and probability theory

• Programming knowledge in Python (preferred)

• Optional: Exposure to reinforcement learning or intelligent systems

Table of Contents

1. Introduction to Agentic AI

• 1.1 Defining Agentic AI

• 1.2 Historical Context and Evolution

• 1.3 Agentic vs. Traditional AI Models

2. Core Principles of Agentic Systems

• 2.1 Autonomy and Goal Orientation

• 2.2 Environment Awareness and Interaction

• 2.3 Planning, Learning, and Decision-Making

3. Paradigms of Agentic AI

• 3.1 Reactive and Deliberative Agents

• 3.2 Hybrid Architectures

• 3.3 Multi-Agent Systems (MAS)

• 3.4 Embodied and Cognitive Agents

4. Architectures and Frameworks

• 4.1 Belief-Desire-Intention (BDI) Model

• 4.2 Markov Decision Processes (MDP)

• 4.3 Reinforcement Learning in Agentic AI

• 4.4 Communication Protocols in MAS

5. Tools and Technologies

• 5.1 Agent-Based Modeling Platforms (e.g., NetLogo, JADE)

• 5.2 Integration with LLMs and Autonomous Agents

• 5.3 OpenAI Agents and LangChain

6. Applications and Case Studies

• 6.1 AI Agents in Robotics

• 6.2 Virtual Assistants and Task Automation

• 6.3 Game AI and Simulation

• 6.4 Agentic AI in Healthcare and Finance

7. Ethical, Social, and Safety Considerations

• 7.1 Ethical Design of Autonomous Agents

• 7.2 Explainability and Trust in Agentic AI

• 7.3 Risk Mitigation and Alignment

8. Future Directions in Agentic AI

• 8.1 Research Frontiers

• 8.2 Human-Agent Collaboration

• 8.3 Agentic AI and AGI (Artificial General Intelligence)

Agentic AI embodies the future of intelligent systems—autonomous, interactive, and capable of complex decision-making. Through this foundational course, learners will gain not only a deep understanding of agentic paradigms and architectures but also a clear vision of how such agents are reshaping industries and research. Equipped with theoretical insights and practical tools, participants will be prepared to innovate and contribute in the rapidly evolving world of Agentic AI.