This module covers the core elements that comprise AI agents, including environments, sensors, models, decision-making logic, and actuators, explaining how they interact to perceive and shape their surroundings.

This module explores various agent types ranging from simple reflex to advanced learning agents, analyzing their key characteristics and capabilities.

This module focuses on how AI agents acquire knowledge and improve, covering human feedback, learning from external systems, and collaborative agent-to-agent learning.

This module details the architectural frameworks of AI agents, distinguishing between non-agentic and agentic workflows, and introducing the ReAct and ReWoo frameworks along with single and multi-agent systems.

This module provides practical strategies for selecting models, configuring instructions, utilizing prompt engineering techniques like few-shot prompting and Chain of Thought reasoning, and evaluating agents with human oversight.

This module guides you through building a hands-on agentic automation using the n8n framework, incorporating system prompts, memory, and integrations with Google Sheets and Gmail.

This module covers the underlying infrastructure of AI agents, exploring APIs, cloud services, data integration, development frameworks, and deployment strategies.

This module examines the business impact and value proposition of AI agents, highlighting how they can be deployed as digital teammates to improve organizational productivity.