Building and Optimizing AI Agent Workflows

This long course equips you with practical knowledge and hands-on skills required to design, architect, and optimize autonomous AI agents that solve multi-step tasks reliably, efficiently, and responsibly. You will study reward-design and reinforcement-learning foundations to translate business objectives into robust reward signals, while learning to evaluate ethical, legal, and societal impacts of agent decision policies. The course covers competing reasoning-loop architectures (e.g., ReAct and Reflexion), modular agent component design with clear APIs, and search and planning strategies (A, beam search, and heuristic augmentation). You will also practice feature engineering and model-interpretability methods to expose spurious correlations and produce explainable agent behaviors. Finally, the course guides you to make strategic modeling choices—such as fine-tuning large models versus training smaller task-specific models—and to package reproducible, reusable ML pipelines for agent subsystems. Throughout the course, practical labs and engineering-focused examples emphasize production-readiness, modularity, and trustworthiness

• Design ethical RL reward functions that align agent behavior and analyze AI's legal and societal implications
• Build modular, scalable agent systems with clear APIs using advanced reasoning-loop architectures like ReAct
• Apply search algorithms & Big-O analysis to optimize pipelines, balancing performance, cost, and success rates
• Build reusable ML pipelines to transform data and apply interpretability techniques to detect model bias

• Basic familiarity with the topic and its common terminology
• Readiness to practice through applied exercises or case-based work