Description

Introduction

As AI advances from reactive systems to autonomous, goal-driven agents, the need to understand how to design and implement agentic AI systems has become critical. These systems integrate perception, reasoning, memory, and decision-making to pursue objectives independently. This course provides a hands-on guide to designing, building, and deploying intelligent agentic systems using current tools and frameworks, with an emphasis on real-world applications, modular architecture, and responsible AI principles.

Prerequisites

This course is designed for intermediate to advanced learners with:

• A foundational understanding of Artificial Intelligence and Machine Learning

• Familiarity with Python programming

• Experience with frameworks like LangChain, OpenAI APIs, or similar

• Basic knowledge of system architecture and software design principles

Table of Contents

1. Introduction to Agentic AI Systems

• 1.1 What Is Agentic AI?

• 1.2 From Reactive to Autonomous Agents

• 1.3 Use Cases Across Industries (e.g., Healthcare, Finance, Research, Robotics)

2. Core Principles of Agent Design

• 2.1 Perception and Environment Modeling

• 2.2 Memory: Short-term, Long-term, and Episodic

• 2.3 Goal Definition and Task Prioritization

• 2.4 Decision-Making and Planning

3. Architectural Blueprints for Agentic AI

• 3.1 Modular Agent Design

• 3.2 Planning Loops (PEAS, OODA, Task Decomposition)

• 3.3 Tool Use, APIs, and External Action Integration

• 3.4 Integrating Feedback and Learning Loops

4. Implementation with Modern Frameworks

• 4.1 Using LangChain, AutoGen, and OpenAI Agents

• 4.2 Implementing Memory Modules with Vector Stores

• 4.3 Chaining and Routing Decisions in Complex Workflows

• 4.4 Setting Up Agents with Tools and Plugins

5. Case Studies and Hands-On Projects

• 5.1 Building a Research Assistant Agent

• 5.2 A Goal-Driven Data Analyst Using LLM + Tools

• 5.3 Multi-Agent Collaboration and Delegation

6. Monitoring and Evaluation

• 6.1 Evaluating Task Success and Agent Performance

• 6.2 Debugging Agentic Behavior

• 6.3 Ensuring Robustness and Reliability

7. Ethical and Safety Considerations

• 7.1 Guardrails for Autonomy

• 7.2 Bias, Hallucinations, and Trust in Agents

• 7.3 Human-in-the-Loop and Override Mechanisms

8. Deployment and Scaling

• 8.1 Hosting and Orchestrating Agentic Systems

• 8.2 Interfacing with External Systems and APIs

• 8.3 Scaling to Multi-Agent Architectures

Building agentic AI systems is a leap toward more intelligent, adaptable, and autonomous applications. By combining structured goals, memory, planning, and external action capabilities, you can design agents that solve complex problems across domains. With this course, you are equipped to not only implement agentic systems but to shape the future of AI development through thoughtful design and responsible deployment.