Male programmer working at his computer with an overlay of computer graphics.

Applied and Agentic AI, DTech

/ Doctorate Degrees / Applied and Agentic AI, DTech

Program at a Glance

Program

Format

Online

English

Program Overview

The Doctor of Technology (DTech) in Applied and Agentic AI is designed for working professionals seeking advanced expertise in artificial intelligence, generative AI, agentic systems, AI infrastructure, and practice-oriented doctoral research. The program combines strong technical foundations with applied research capability, enabling learners to design, evaluate, implement, and improve AI-enabled systems in real-world organizational and industry contexts.

The Doctor of Technology in Applied and Agentic AI requires completion of 27 units of major courses, 14 units of dissertation foundation courses, and 28 units of dissertation work, for a total of 69 units. The program is particularly valuable for C-suite technology leaders (e.g. CTOs, CIOs, CDOs), AI practitioners, senior engineers, product and platform leaders, consultants, researchers, entrepreneurs, and professionals seeking to advance into senior operational, consulting, and advisory roles that require deep expertise in AI system design, enterprise GenAI implementation, agentic AI workflows, AI infrastructure, and evidence-based technology innovation. They are able to oversee the building and maintenance of highly efficient, effective, and responsible technology, AI stacks, and intelligence infrastructures.

To achieve these skills and knowledge, learners progress from foundational AI, machine learning, deep learning, software systems, Generative AI, enterprise application design, agentic AI, and AI infrastructure into a structured doctoral research phase focused on solving a real-world problem of practice through a Dissertation in Practice.

Learning Outcomes

Upon completion of the Doctor of Technology (DTech) in Applied and Agentic AI 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.

Curriculum

The Doctor of Technology in Applied and Agentic AI requires completion of 14 units of foundation courses (math proficiency and technical foundations), 19 units of core courses (15 units of advanced technical courses, 4 units of Bridge and 0 units of Qualifying Exam), and 36 units of advanced graduate courses (28 units of dissertation work and 8 units of dissertation research and analysis courses), for a total of 69 units. Students must earn a “B-” or better in each course and a cumulative grade-point average of 3.00 or better.

The major assessment points in the DTech program are the qualifying examination and the dissertation research milestones. Students must receive a passing score on the qualifying examination and successfully complete all required courses before they are allowed to present a dissertation proposal and officially advance to candidacy. Students must complete and successfully defend their dissertations within five years of beginning the program.