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Applied and Agentic AI, MS

/ Master’s Degrees / Applied and Agentic AI, MS

Program at a Glance

Program

Format

Online

English

Program Overview

The 32-unit MS in Applied and Agentic AI is designed for working professionals who want to build advanced applied capability in artificial intelligence, generative AI, agentic systems, AI-enabled software systems, and AI infrastructure. This graduate-level program combines strong technical foundations with practical application, enabling learners to design, evaluate, and apply AI-enabled solutions for real-world business, technology, and organizational contexts.

The M3A program is particularly valuable for software engineers, data professionals, AI practitioners, technology consultants, product and platform professionals, and early-to-mid career technology professionals seeking to deepen their expertise in machine learning, deep learning, Generative AI, enterprise AI applications, agentic workflows, and production-ready AI systems. Learners progress from foundational AI, mathematics, statistics, algorithms, machine learning, deep learning, and software systems into advanced areas such as Generative AI, enterprise GenAI application design, agentic AI systems, and data engineering for LLMs and agents. The program culminates in a Capstone, where learners integrate their learning into a practical AI-enabled solution or implementation-oriented project.

Learning Outcomes

Upon completion of the MS in Applied and Agentic AI (M3A) program, graduates will be able to demonstrate applied skills and knowledge of AI:

Foundations: Apply mathematics, statistics, Python programming, algorithms, search and sequential decision-making, and AI-enabled software systems.
Models: Build, train, evaluate, and improve machine learning and deep learning models using supervised and unsupervised learning, feature engineering, ensemble methods, recommender systems, causal inference and decision systems, time series forecasting, neural networks, back propagation, CNNs, RNNs, graph neural networks, attention mechanisms, and transformers.
Applications: Design and evaluate enterprise GenAI applications using foundation models, embeddings, multimodal AI, instruction tuning, fine-tuning, prompt engineering, RAG, vector databases, grounding, and provenance, applying responsible AI practices including alignment, safety, and bias/fairness evaluation.
Systems: Design and evaluate agentic AI systems and production-ready AI workflows incorporating planning, memory, tool use, reflection, human-in-the-loop workflows, multi-agent collaboration, MCP, A2A, enterprise interoperability, and responsible AI guardrails for autonomous and multi-agent behavior.
Production Engineering and Security: Engineer AI data infrastructure (pipelines, retrieval, knowledge graphs) and deploy, scale, secure, and govern production AI systems through MLOps, LLMOps, distributed training and inference, observability, cost/capacity engineering, and responsible AI practices including access control, sandboxing, adversarial defenses, privacy protection, and governance.
Integration: Integrate learning across AI, GenAI, agentic systems, software systems, data engineering, and AI infrastructure through a capstone project that demonstrates practical problem-solving, implementation thinking, system-level design, and responsible AI assessment.

Curriculum

The Master of Science in Applied and Agentic AI requires completion of 29 credits in the core program and 3 credits in the capstone, for a total of 32 credits. For further information regarding graduation requirements, see Academic Requirements for Graduate Programs.