Enterprise AI has moved past chatbots and content generation into agentic AI. These systems plan a task, pull the tools or data they need, and carry it through on their own, with only occasional human sign-off. Gartner projects 40% of enterprise applications worldwide will embed task-specific AI agents by the end of 2026, up from under 5% in 2024. Yet McKinsey's global research shows a gap behind that: nearly two-thirds of enterprises have experimented with AI agents, but fewer than 10% have scaled them to deliver measurable value. That gap exists because most professionals lack structured, applied exposure to the skills agentic AI runs on, from Python and LLMs to RAG and multi-agent design.
India is part of that same shift. EY's AIdea of India 2026 report, surveying 200 Indian enterprises, found 24% of leaders already deploying agentic AI. Closing that gap takes more than self-study, and three global universities, McCombs School of Business at the University of Texas at Austin (Texas McCombs), Johns Hopkins University (JHU), and MIT Professional Education (MIT PE), have each built programs with Great Learning as edtech partner that take working professionals from no-code AI workflows to advanced, production-grade multi-agent systems.
This 12-month program from Texas McCombs covers the full spectrum of AI and Machine Learning, from foundational concepts through Generative AI and Agentic AI. The curriculum includes 11 hands-on projects and 60+ case studies, and learners can also opt for a 6-month fast-track version for a shorter, structured experience.
● Format: 12 months online, recorded video lectures, live monthly masterclasses by Texas McCombs faculty, and interactive mentorship sessions with industry experts
● Credential: Dual certificates from Texas McCombs and Great Lakes Executive Learning, plus 9.5 Continuing Education Units (CEUs)
● Fee: ₹ 2,75,000 + GST
● What you get: Hands-on expertise in Python and ML, image processing and CNNs for visual recognition, design of single-agent and multi-agent systems, SQL querying, RAG pipelines, DevOps, MLOps, and LLMOps practices including CI/CD pipelines
● Who it is for: Professionals in tech-adjacent roles transitioning into AI/ML careers, tech practitioners and technical leaders building and deploying AI solutions, business leaders and functional heads deploying scalable AI systems, and early-career professionals building a foundation in AI/ML
This 13-week program from Texas McCombs lets learners choose between a code track (Python-based) and a no-code track (built on tools such as n8n, NotebookLM, ChatGPT, and Gemini), completing all hands-on components in their chosen track. The curriculum covers LLMs, Prompt Engineering, RAG, and the design of single-agent and multi-agent systems for business applications.
● Format: 13 weeks online, recorded video lectures, live sessions with global industry experts, live monthly masterclasses by Texas McCombs faculty, choice of code or no-code track
● Credential: Certificate of Completion from the Texas, plus CEUs
● Fee: ₹ 1,70,000 + GST
● What you get: Navigating the AI landscape, building context-aware single-agent systems with tools and memory, applying planning and reasoning for autonomous agents using LLMs and Prompt Engineering, implementing RAG systems and designing scalable multi-agent ecosystems
● Who it is for: Knowledge professionals building practical Agentic AI and GenAI skills, business and tech experts expanding their GenAI foundation, functional professionals and managers automating workflows, aspiring AI practitioners, and technical leaders driving GenAI adoption within their teams
This 18-week program from JHU follows a project-driven path, starting with Python programming and LLMs before progressing to single- and multi-agent systems. Learners secure their systems against real-world vulnerabilities and deploy them according to monitoring and reliability standards used in production environments, supported by access to OpenAI API keys from Great Learning.
● Format: 18 weeks online, 8-10 hours per week, recorded video lectures, 16+ live sessions with industry mentors, 4 masterclasses by JHU faculty, and 1 masterclass by an industry expert
● Credential: Certificate of Completion and 13 CEUs from JHU, and a shareable e-portfolio
● Fee: 1,75,000 + GST
● What you get: Building AI prototypes in Python, understanding AI from ML to LLMs, generating reliable outputs via prompting and RAG, building autonomous agents, scaling to multi-agent workflows, evaluating AI performance and hallucinations, securing AI with guardrails and running AI in production with monitoring
● Who it is for: STEM professionals in technical or tech-adjacent roles, data and AI professionals (analysts, data scientists, AI/ML engineers), technical managers and product managers, and new entrants to technology (via a structured Python pre-work module)
This 16-week program from JHU is designed to build real AI tools with no prior ML experience required. It combines Python, Prompt Engineering, RAG, fine-tuning, and Agentic AI workflows, and includes access to OpenAI API keys from Great Learning along with masterclasses on multi-modal AI applications and Anthropic.
● Format: 16 weeks online, 8-10 hours per week, recorded video lectures plus weekly interactive mentored learning, 12+ live sessions with industry mentors, 4 masterclasses by JHU faculty, and 2 additional masterclasses by industry experts
● Credential: Certificate of Completion from JHU, plus 11 CEUs
● Fee: ₹ 1,70,000 + GST
● What you get: Building AI tools like chatbots, classifiers, and summarizers using Python and AI APIs, understanding LLMs, GenAI, and agents; building multi-agent systems, identifying bias, hallucinations, and legal exposure
● Who it is for: Tech professionals building and deploying AI-driven solutions, data professionals (analysts, engineers, data scientists), technology consultants and technical managers, and STEM graduates upskilling into GenAI
This 12-week program from JHU requires no coding experience and is built around no-code platforms such as n8n. It covers GenAI workflows, RAG, multi-agent systems, and Responsible AI practices, with exposure to leading models including Anthropic's Claude through a self-paced module and dedicated masterclass.
● Format: 12 weeks online, 8-10 hours per week, no coding required, recorded video lectures, 11+ live sessions with industry mentors, 3 masterclasses by JHU faculty, and 1 masterclass by an industry expert
● Credential: Certificate of Completion and 9 CEUs from JHU
● Fee: ₹ 2,85,000
● What you get: Understanding key concepts in NLP, GenAI, and LLMs; identifying strategic business opportunities and use cases for GenAI and AI agents across sectors, understanding Responsible AI principles, using no-code platforms and RAG to build AI-powered workflows connected to business data
● Who it is for: Business leaders and functional heads (sales, marketing, finance, operations) automating enterprise-wide workflows, technology professionals and team leads prototyping and orchestrating multi-agent systems and RAG pipelines, and subject matter experts in compliance-heavy or knowledge-intensive domains
This 14-week program from MIT PE is designed for professionals without a technical background, enabling them to design and implement AI-driven solutions without writing code. Learners progress from core AI concepts to designing and deploying multi-agent systems using no-code tools, supported by access to OpenAI API keys and an n8n lab from Great Learning.
● Format: 14 weeks online (no coding required), 20 hours of recorded video lectures from MIT faculty and 14+ live mentorship sessions with industry experts
● Credential: Certificate of Completion from MIT PE, plus 10 CEUs
● Fee: ₹ 2,10,000 + GST
● What you get: Understanding AI from classical ML to autonomous agents, building RAG pipelines, applying Prompt Engineering to LLMs, designing and testing AI workflows with no-code tools, building autonomous agents, orchestrating multi-agent systems, applying clustering, classification, and regression with no-code tools and evaluating AI output accuracy and reliability
● Who it is for: Business leaders and functional heads leading AI initiatives, business analysts and product managers building rapid AI prototypes, functional managers across marketing, operations, legal, and finance, and entrepreneurs or independent consultants
Choosing from these AI programs comes down to two questions: how much time can you commit, and do you want to build with code or without it. The shorter, no-code options from JHU and MIT Professional Education suit business leaders and functional managers who need to design and run AI workflows without a technical background, while the longer, code-first programs from Texas Mccombs and JHU are built for those looking to work directly with Python, LLMs, and production-grade multi-agent systems. All programs address the same underlying problem: the skills gap that Gartner, McKinsey, and EY all point to is real. A structured, university-backed program gives working professionals a direct path to close it and move from AI users to AI builders.
Program details, fees, and curricula are subject to change. Confirm current information with the program provider before applying.
Disclaimer: This article is published in association with MyGreatLearning and not created by TNM Editorial.