Architect Reusable AI Agent Systems

Overview

Architect Reusable AI Agent Systems is an intermediate course for AI engineers and developers who want to move beyond building single-purpose agents and learn to design scalable, modular agent architectures that drive business value. This course teaches advanced system design principles, equipping you to build maintainable AI systems that can evolve with business needs. You will learn to evaluate competing reasoning-loop architectures like ReAct and Reflexion by running data-driven A/B tests to select the optimal design for a specific use-case. Through hands-on labs, you will master the software engineering best practices for creating reusable agent components—the Planner, Memory, and Executor—by defining clear API contracts with typed interfaces. You will leave this course with the ability to design, document, and implement a complete Python package of agent components, ready for integration into a production environment.

Syllabus

Comparing Agent Reasoning Frameworks

This module focuses on the critical skill of architectural selection. You will learn that not all agent "brains" are created equal. You will dive into two popular reasoning frameworks, ReAct and Reflexion, and learn how to use empirical data from A/B tests to make a justified recommendation for which architecture best suits a specific, real-world use case.

Modular Component Design for Reusability

This module shifts your focus from being an analyst to being an architect. You will learn the software engineering best practices that enable scalable and maintainable AI systems. You will design the core components of an agent—Planner, Memory, and Executor—and define the clear API contracts that allow them to work together seamlessly.