Agentic AI Fundamentals: Architectures, Frameworks, and Applications

Course details

Looking to dive into the world of agentic AI? The course covers the essential concepts, architectures, and frameworks that form the foundation of autonomous agents. Explore decision-making processes, machine learning techniques, and multi-agent systems and gain practical knowledge to develop and deploy agentic AI solutions. Instructor David Linthicum also addresses the ethical and societal implications of agentic AI, as well as security and robustness considerations. Check out this course to get equipped with the skills and understanding needed to leverage agentic AI in various industries and applications.

Learning objectives
Understand the fundamentals of agentic AI, including agent architectures, decision-making frameworks, and environment interaction.
Learn how to implement machine learning techniques, such as reinforcement learning, to enable agents to learn and adapt.
Gain knowledge of multi-agent systems, communication protocols, and negotiation strategies.
Discover practical applications of agentic AI in industries such as robotics, finance, healthcare, and transportation.
Develop an understanding of the ethical and societal implications of agentic AI and learn how to design secure and robust agents.

Concepts

0. Introduction

01 - Agentic AI - The future of smart AI is here

1. Definition and Importance of Agentic AI

02 - What is agentic AI
03 - Applications and use cases of agentic AI
04 - Healthcare scenario

2. Agentic Behavior and Adaptation

05 - How AI systems perceive their environment
06 - How AI agents make decisions

3. Core Methodology and Tools in Agentic AI

07 - Machine learning for agents
08 - Policy frameworks
09 - Utility-based frameworks
10 - Reinforcement learning and Q-learning

4. Agent Architectures in Agentic AI

11 - Agent architectures - major
12 - Agent architectures - minor

5. Advanced Communication Frameworks in Agentic AI

13 - Developing a cooperative multiagent system
14 - Implementing communication protocols for agents
15 - Game theory and strategic interaction
16 - Negotiation and conflict resolution

6. Ethical and Societal Implications

17 - Ethical frameworks and considerations in AI
18 - Societal impact of autonomous agents

7. Security and Robustness

19 - Security challenges
20 - Robustness and fault tolerance

8. Agentic AI in the Future

21 - The future role of agentic AI in industry 4.0 and beyond
22 - Integration with other technologies

Conclusion

23 - Next steps