Learn Agentic AI: From Basics to Advanced Multi-Agent Systems

This comprehensive course on Agentic AI offers a deep dive into the evolution of artificial intelligence from traditional, rule-based systems to dynamic, autonomous agents capable of independent action and learning. Designed for intermediate learners, the course bridges theoretical concepts with practical applications, ensuring a holistic understanding of modern AI paradigms. You will start by exploring the inherent limitations of conventional AI, which often struggles with adaptability and real-time decision-making, setting the foundation for why agentic and generative approaches are revolutionizing the field. The initial lessons introduce Agentic AI and Generative AI, clarifying their definitions, differences, and synergies, and how their integration can lead to more intelligent, responsive systems that mimic human-like reasoning and creativity.

As the course progresses, it delves into the core components of agentic systems, including AI agents and LLM agents, with detailed explanations and examples to illustrate their roles in various contexts. The theoretical knowledge is immediately reinforced through hands-on tutorials, beginning with the construction of a simple reflex agent in Python to simulate a smart room cleaning scenario. This practical approach continues with goal-based agents using Langchain and Streamlit, where you'll learn to design agents that pursue specific objectives in interactive environments, fostering skills in coding and system design.

The middle section focuses on learning agents and reinforcement learning, a critical area for developing adaptive AI. You'll grasp the basics of reinforcement learning, understand value functions through the Bellman equation, and master Q-learning with step-by-step Python implementations. These lessons demystify complex algorithms, enabling you to implement learning mechanisms that allow agents to improve over time based on environmental feedback. The course then transitions to advanced topics, covering powerful frameworks and protocols that are shaping the future of agentic AI, such as LangGraph for building agentic workflows, CrewAI as an agentic framework, and Agno for multimodal agentic AI.

In the latter part, the Model Context Protocol (MCP) is thoroughly explored, from its fundamentals to practical applications. You'll learn how to run and connect multiple MCP servers, create custom servers with LangGraph and Streamlit, and deploy them on the cloud, ensuring scalability and robustness. The course concludes with integration techniques, demonstrating how to use MCP with Agno and the OpenAI Agents SDK to build sophisticated, real-world agentic AI applications. By covering everything from foundational concepts to cloud deployment, this course equips you with the knowledge and skills to innovate in autonomous systems and tackle contemporary AI challenges.

Key Topics Covered in This Course:
- Analysis of traditional AI limitations and the rationale behind Agentic AI's emergence.
- Core definitions and synergies between Generative AI and Agentic AI for enhanced system capabilities.
- Exploration of AI agents, LLM agents, and various agent types with practical examples and use cases.
- Hands-on projects: building reflex and goal-based agents in Python using Langchain and Streamlit for real-time applications.
- Foundational reinforcement learning: value functions, Bellman equation, and Q-learning with Python tutorials to reinforce adaptive learning.
- Advanced frameworks: LangGraph for agentic workflows, CrewAI, and Agno for multimodal AI system development.
- In-depth coverage of MCP: creating, connecting, and deploying custom servers locally and on the cloud for scalable solutions.
- Integration strategies for MCP with Agno and OpenAI Agents SDK to develop comprehensive, deployable AI systems.