Applied and Agentic AI, MS to DTech Pathway

The 40-unit Doctor of Technology in Applied and Agentic AI Pathway program is designed for learners who have completed the GGU Master of Science in Applied and Agentic AI degree and wish to progress into advanced doctoral-level applied research. The pathway enables learners to extend their technical foundation in AI, Generative AI, agentic systems, and AI infrastructure to the completion of a rigorous practice-based doctoral degree.

This pathway is particularly valuable for AI practitioners, technology professionals, senior engineers, product and platform leaders, consultants, researchers, and technology leaders who want to investigate real-world problems of practice and contribute applied, evidence-based solutions in the field of artificial intelligence.

Learners enter the pathway through a doctoral bridge and assessment course after completing the master’s-level AI curriculum and move into a structured doctoral phase focused on topic development, doctoral research methods, proposal defense, and final dissertation completion. The pathway culminates in a Dissertation in Practice, where learners identify a meaningful AI-related problem, investigate it using appropriate research methods, and develop insights, interventions, or solutions with practical relevance and scholarly rigor.

Upon completion of the Doctor of Technology in Applied and Agentic AI Pathway program, graduates will be able to demonstrate applied skills and knowledge of advanced:

• Foundations: Applying mathematics, statistics, programming, algorithms, machine learning, deep learning, software systems, data engineering, and AI infrastructure to design and build advanced AI-enabled systems.
• Applications: Designing and evaluating Generative AI and enterprise AI applications, including foundation models, transformers, embeddings, multimodal AI, model adaptation, prompt engineering, retrieval-augmented generation, grounding, and provenance.
• Systems: Designing and evaluating agentic AI systems and workflows, including planning, memory, tool use, reflection, human-in-the-loop control, multi-agent collaboration, MCP, A2A, enterprise interoperability patterns, and production-ready lifecycle practices.
• Doctoral Inquiry: Distinguishing levels of contribution from implementation to doctoral innovation, framing significant doctoral-level research problems with clearly articulated assumptions and significance, and critically synthesizing scholarly, technical, practitioner, and regulatory evidence to identify and position work within frontier research debates.
• Research Methodology and Responsible Practice: Designing rigorous research and evaluation plans (datasets, baselines, experiments, benchmarks, ablations, and metrics) with statistical validity and reproducibility, and applying responsible AI, governance, legal, security, and dual-use considerations to doctoral research.
• Solutions: Design and implementation of innovative AI solutions through a Dissertation in Practice demonstrating scholarly rigor, professional relevance, ethical awareness and responsibility, stakeholder-oriented communication, and real-world impact.

Students must have completed a 32-unit GGU MS in Applied and Agentic AI. The pathway adds 12 units of dissertation foundation courses and 28 units of dissertation-phase courses to the completed DTech curriculum.