This course introduces the foundations of AI agent design by starting with the structure of a single agent. Unlike traditional chatbots, agents are capable of reasoning, acting, and reflecting within a system loop, allowing them to make decisions based on context.
Learners will explore the internal structure of a single agent, including how memory, prompts, user inputs, outputs, and reflections contribute to continuous conversational awareness. The course also explains the ReAct pattern, which integrates reasoning and acting into a unified process, and introduces the TAO loop (Translate–Act–Observe) as a framework for understanding the phases of agent operation. Finally, learners will examine the role of reflection and metacognition, and how these processes allow agents to evaluate their actions and adapt over time.
By the end of this course, you will be able to:
Understand the structure of a single agent
Explain the ReAct pattern
Describe the phases of the TAO agent loop
Recognize the importance of reflection and metacognition in AI agents
A solid foundation in computer programming principles
Intermediate knowledge of the python programming language