Description

Introduction

Artificial Intelligence systems can broadly be classified into reactive and agentic paradigms. Reactive AI operates based on immediate stimuli without internal goals or memory, making it fast and simple—but limited in complexity. In contrast, Agentic AI systems pursue long-term goals, use memory, plan actions, and adapt over time. This course offers a comparative framework to understand the principles, use cases, and design implications of both approaches, enabling practitioners to choose the right model for specific AI problems.

Prerequisites

This course is suitable for learners with:

• A basic understanding of AI/ML principles

• Familiarity with algorithms and decision-making processes

• Optional: Experience with AI toolkits (e.g., OpenAI, TensorFlow, LangChain)

• No prior experience in agent-based systems required, but helpful

Table of Contents

1. Introduction to Reactive and Agentic AI

• 1.1 What Is Reactive AI?

• 1.2 What Is Agentic AI?

• 1.3 Historical Context and Evolution

2. Core Characteristics Comparison

• 2.1 Perception and Response Models

• 2.2 Goal Orientation and Planning

• 2.3 Memory and Adaptability

3. Architecture and System Design

• 3.1 Reactive Systems: Rule-Based Engines and State Machines

• 3.2 Agentic Systems: Deliberative, Hybrid, and BDI Models

• 3.3 Control Loops and Feedback Mechanisms

4. Example Applications

• 4.1 Reactive AI in Chatbots, Robotics, and Automation

• 4.2 Agentic AI in Autonomous Vehicles, Research Agents, and Virtual Assistants

• 4.3 Case Study: Reactive vs. Agentic in Game AI

5. Performance and Scalability

• 5.1 Latency and Responsiveness

• 5.2 Resource Requirements

• 5.3 Adaptability in Dynamic Environments

6. Design Trade-Offs and When to Use Each

• 6.1 Simplicity vs. Complexity

• 6.2 Determinism vs. Emergence

• 6.3 Use Case Suitability Matrix

7. Hybrid Systems: Best of Both Worlds

• 7.1 Combining Reactive and Agentic Components

• 7.2 Architecting Layered Intelligence

• 7.3 Practical Frameworks and Examples

8. Ethical and Safety Considerations

• 8.1 Predictability vs. Autonomy

• 8.2 Controllability and Oversight

• 8.3 Risk Management in Agentic Systems

Understanding the distinctions between reactive and agentic AI helps align system design with task complexity and strategic goals. Reactive AI is suitable for straightforward, rule-based responses, while Agentic AI enables flexible, autonomous behaviors suitable for dynamic or open-ended problems. Choosing the right framework—or blending both—ensures smarter, safer, and more efficient AI system development.