Temple University 3 credits CIS
This graduate-level course provides comprehensive coverage of modern agentic systems, combining theoretical foundations with hands-on implementation experience. Students will explore the evolution from classical agent architectures to contemporary large language model-based agents, mastering both the mathematical underpinnings and practical deployment considerations. The course emphasizes research-driven learning, requiring students to engage with cutting-edge literature while building production-ready systems. Topics span agent theory, multi-agent coordination, advanced reasoning techniques, retrieval-augmented generation, and enterprise deployment strategies. Students will develop expertise in modern frameworks while understanding formal models and safety considerations essential for responsible AI agent development. Note: To register for this course, students should have earned a grade of "C-" or better in college-level Python Programming, Computational Probability and Statistics, Design and Analysis of Algorithms, and Machine Learning.
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