This foundational lesson introduces what AI agents are and how they differ from traditional software. Learners will explore agent-environment interactions, the concept of perception, and how various types of agents—reactive, deliberative, and hybrid—handle decision-making. Through real-world examples like smart assistants and warehouse robots, learners will classify agent types and determine where each model excels or breaks down.

This lesson moves from theory to implementation. Learners will construct intelligent agents that integrate inputs (perception), structured reasoning (decision loops), and output (action). They'll explore core modules such as memory, planning chains, and tool execution in LangChain and Rasa. Real-world examples like Alexa’s task-based updates and LangChain agents with tools will help frame the technical walkthroughs.

In the final lesson, learners will focus on evaluating how agents perform in realistic, changing environments. They'll explore testing strategies, interpret edge-case behaviors, and fine-tune agents using logs, performance feedback, and outcome tracking. Examples such as AlphaCode’s reasoning iterations and BabyAGI’s task queue refinement will help frame the concepts. This lesson culminates in the Capstone project, where learners will apply everything they've learned to design and deliver an intelligent, goal-driven agent.