In this module, we will explore the core principles that define AI agents and their capabilities. You will evaluate when and where AI agents can be applied. Finally, you will gain practical experience by designing your first simple AI agent.

In this module, we will dissect the anatomy of AI agents, examining their brains, senses, memory, actions, and communication. You will understand how the Agentic Loop drives agent behavior. Hands-on exercises will enable you to build real-world AI agents.

In this module, we will introduce Agentic AI and highlight its distinguishing features. You will explore system-level architectures and multi-agent system designs. Hands-on exercises will guide you in designing orchestration and choreography models.

In this module, we will explore the transition from traditional to agentic workflows. You will examine the components, patterns, and frameworks that enable efficient agentic systems. Practical exercises will help you build workflows in real-world scenarios.

In this module, we will survey the frameworks that support Agentic AI development. You will explore tools like LangChain, LlamaIndex, Microsoft Agent Framework, and cloud-based platforms. Practical insights will enable you to select and combine frameworks effectively.

In this module, we will explore the rationale behind Agentic AI design patterns. You will examine patterns for task management, collaboration, and safety. Hands-on exercises will allow you to apply these patterns in real-world agentic scenarios.

In this module, we will examine the constraints of traditional RAG systems. You will explore the improvements offered by Agentic RAG. Practical exercises will illustrate how the Active Researcher loop enhances retrieval performance.

In this module, we will address the “Protocol Problem” in Agentic AI. You will explore different communication protocols, including ACP, A2A, AG-UI, and MCP. Architectural insights will help you implement effective communication systems.

In this module, we will introduce the Model-Context Protocol (MCP) and its role in Agentic AI. You will explore MCP architecture, lifecycle, and core concepts. Real-world examples and design patterns will demonstrate its practical application.

In this module, we will explore the critical role of context in AI systems. You will examine types of context and how they influence agent behavior. Practical strategies and use cases will illustrate effective context engineering.

In this module, we will guide you through the AI agent development lifecycle. You will explore evaluation, AgentOps, and enterprise considerations. Hands-on exercises will help you design and manage agentic AI platforms effectively.

In this module, we will review your journey through Agentic AI architectures. You will consolidate your knowledge of design patterns, frameworks, and MCP. Finally, you will reflect on practical applications for future projects.