Solve Business Problems with Agentic AI

Post Graduate Program in AI Agents for Business Applications

Application closes on 31st Mar 2026

Delivered in collaboration with Great Learning

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Post Graduate Program in AI Agents for Business Applications

Application closes 31st Mar 2026

Program Outcomes

Elevate Your Career with AI

Build Agentic AI workflows to solve business problems and drive growth

Navigate the AI landscape and understand foundational concepts to address business challenges across functions
Apply GenAI, large language models, and Retrieval-Augmented Generation to enhance business productivity
Develop intelligent, context-aware single-agent systems to automate workflows and drive operational efficiency
Solve business problems with planning and reasoning strategies, and scalable, secure multi-agent ecosystem

Earn a Certificate of Completion from McCombs School of Business

#1 (U.S., Big Data Management) in MS Business Analytic

Eduniversal (2025)
#6 Analytics

U.S. News & World Report (2025)
#6 Business Programs

U.S. News & World Report (2025)
#7 in MS - Business Analytics

QS World University Rankings (2022)
#7 in MS Business Analytics

The Financial Engineer Times (2025)
#3 in Information Systems Graduate Programs

U.S. News & World Report (April 2025)
#7 Public University in the U.S.

U.S. News & World Report, 2026
#6 in Executive Education - Custom Programs

Financial Times, 2022

Key program highlights

Why Choose This Program

Learn from renowned faculty and experts

Learn from recorded lectures and monthly live masterclasses by Texas McCombs faculty, and live, mentor-led sessions where industry experts present real-world case studies
Choose to learn with or without code

Choose between a Python-based coding track or a no-code, tools-based track, and complete hands-on components using aligned technologies.
Hands-on learning

Build industry-ready skills for creating intelligent Agentic AI systems using industry-relevant tools, projects, and real-world case studies across sectors
Learning support

Get personalized assistance from a dedicated program manager, academic support through the Great Learning community, project discussion forums, and peer groups
Create a compelling e-portfolio

Develop an industry-ready portfolio that showcases your mastery of skills and tools
Earn recognized credentials

Upon program completion, earn a globally recognized certificate of completion from the McCombs School of Business at The University of Texas at Austin

Skills you will learn

RAG

AGENTIC RAG

MCP Framework

Multi-Agents System

Responsible AI

Agentic AI

Generative AI

Large Language Models

Prompt Engineering

RAG

AGENTIC RAG

MCP Framework

Multi-Agents System

Responsible AI

Agentic AI

Generative AI

Large Language Models

Prompt Engineering

Overview
Curriculum
Tools
Faculty
Fees

This Program Is Ideal for

Professionals across career stages seeking a flexible learning track to build Agentic AI systems

Knowledge professionals

Looking to develop practical, industry-ready skills in Agentic AI to automate and optimize workflows
Business and tech experts

Seeking to expand their knowledge in designing intelligent agentic systems to enhance decision-making and operational efficiency
Aspiring AI Practitioners

Preparing to contribute effectively to projects involving Agentic AI for process automation and intelligence
Technical Leaders

Aiming to guide their teams in translating business workflows into Agentic AI workflows to drive innovation and transformation

Comprehensive Curriculum

Designed by Texas McCombs faculty, this curriculum offers a hands-on foundation in AI Agents. It covers Python, GenAI, Large Language Models, and Retrieval-Augmented Generation. Participants learn to build intelligent AI agents using tools, memory, planning, and reasoning, progressing to secure, scalable multi-agent systems for business application

Code or No-Code

Flexible learning tracks
20+

Tools and techniques
2.5 CEUs

Upon program completion

PRE-WORK

This preparatory module is designed to help learners navigate the AI landscape by identifying key problem areas and solution opportunities. It enables participants to understand how businesses can effectively leverage AI technologies while building a foundational understanding of the tools and frameworks required to develop Agentic AI solutions that support strategic business initiatives.

Introduction to AI Landscape

1. Introduction to key terminology (Artificial Intelligence, Machine Learning, Deep Learning, Generative AI, Large Language Models, Agentic AI) 2. History and evolution of AI 3. Business problems and solution spaces across different industries

Hands-On Tool Introduction and Setup

Code Tools 1. Introduction to Python 2. Environment setup: VS Code, Google Colab 3. Fundamental Python programming constructs: variables, data types, data structures (list, dictionary, tuple), conditional and looping statements, functions 4. OOP basics: classes, objects, inheritance No-Code Tools 1. Introduction to no-code tools 2. Environment setup: GL N8N Labs 3. Core functionality and UI overview

MODULE 01: AGENTIC AI FOUNDATIONS

This module focuses on building a strong foundation in Generative AI and Large Language Models by differentiating them from discriminative approaches, understanding how LLMs work and their enterprise applications, practicing prompt engineering with techniques and templates to improve reliability and scalability, and exploring Retrieval-Augmented Generation (RAG) and its key components to overcome the limitations of prompting and develop context-aware, enterprise-ready AI solutions.

Introduction to Generative AI and Large Language Models

1. Generative AI vs. Discriminative AI 2. Overview of LLMs 3. Interacting with Generative AI 4. Risks of Generative AI 5. Business applications of Generative AI

Prompt Engineering and Retrieval Augmented Generation

1. The need for Prompt Engineering 2. Common prompting techniques (Zero-shot, One-shot, Few-shot, Chain-of-Thought) 3. Best practices for crafting effective prompts 4. Reusable prompt templates 5. The need for RAG 6. Key components of RAG (data chunking, embeddings, vector store, retrieval, augmentation, generation)

Project Week

MODULE 02: BUSINESS APPLICATIONS WITH AGENTIC AI

This module focuses on understanding and developing AI agents, equipping learners with both foundational insights and advanced techniques in Agentic AI implementation. It begins with an introduction to AI agents, where you explore the basic concepts and frameworks that define intelligent agents. The module then delves into how tools and memory can be incorporated into agents to enhance their functionality and efficiency in task execution. Finally, it covers planning and reasoning, teaching you how agents can be programmed to make informed decisions and solve complex problems autonomously.

Introduction to AI Agents

1. The need for AI agents 2. Types of agents 3. Agent environments 4. Grounding and validation 5. Building simple AI agents with LangChain 6. Agentic RAG

Incorporating Tools and Memory in Agents

1. The need for external tools 2. Types of tools 3. The need for memory 4. Short-term vs Long-term memory 5. Introduction to MCP 6. Tool-based agents with MCP

Planning and Reasoning

1. The role of planning 2. Self-reflection 3. The role of reasoning 4. Multi-step reasoning 5. Task decomposition 6. Introduction to the ReAct framework

Learning Break

Project Week

MODULE 03: ADVANCED AGENTIC AI SOLUTIONS

This module explores advanced concepts in Agentic AI, starting with multi-agent systems, where learners examine the interaction and coordination between multiple AI agents to solve complex problems collaboratively. Next, the focus shifts to testing and evaluating agentic systems, providing insights into methodologies for assessing the performance, reliability, and effectiveness of AI agents in various scenarios. Finally, the module covers the crucial topic of securing agentic AI solutions, highlighting the importance of implementing robust security measures to protect AI systems from vulnerabilities and ensure safe deployment in real-world applications.

Multi-Agent Systems

1. The need for multi-agent systems (specialization and expertise, scalability, parallel processing, security and fault tolerance) 2. Architecture of a multi-agent system 3. Designing a multi-agent system

Testing and Evaluation of Agentic Systems

1. Unit testing 2. Integration testing 3. System testing 4. Multi-agent testing 5. Evaluation metrics (accuracy, latency, robustness) 6. Grounding, validation, and truthfulness 7. Human-in-the-loop evaluation

Securing Agentic AI Solutions

1. Data security and privacy 2. Agent behavior security 3. Logging decision-making for transparency 4. Access control and identity 5. Regulatory compliance and ethical considerations 6. Deploying a single/multi-agent system as a web app

Project Week

SELF-PACED COURSE

Multimodal Agentic AI (Masterclass only)

This masterclass builds a foundational understanding of multimodal Agentic AI systems, focusing on how models integrate and align information across text, vision, and other modalities through cross-modal reasoning and attention. 1. Multimodal Foundation Models 2. Cross-Modal Reasoning, Attention & Alignment

Learn In-Demand AI Tools and Techniques

Foundational and advanced tools for Agentic AI to solve complex business challenges

n8n
LangChain
LangChain ReAct
LangGraph
LangSmith
Hugging Face
ChromaDB
MCP Framework
Streamlit
Pandas
FAISS
Sentence Transformers
Dynabench
Google Colab
Transformers
Llama Cpp
Gemini
NotebookLM
ChatGPT
Python

Meet Your Faculty

Learn from the top, world-renowned faculty at The University of Texas at Austin

Dr. Kumar Muthuraman

Faculty Director, McCombs School of Business, The University of Texas at Austin

Faculty Director, Center for Analytics and Transformative Technologies

21+ years' experience in AI, ML, Deep Learning, and NLP.

Know More
Dr. Daniel A Mitchell

Clinical Assistant Professor, McCombs School of Business, The University of Texas at Austin

Research Director, Center for Analytics and Transformative Technologies

15+ years of experience in financial engineering and quantitative finance.

Know More

Course Fees

Invest in your career

Apply GenAI, large language models, and Retrieval-Augmented Generation to enhance business productivity
Develop intelligent, context-aware single-agent systems to automate workflows and drive operational efficiency
Solve business problems with planning and reasoning strategies, and scalable, secure multi-agent ecosystem
Design, evaluate, and secure multi-agent ecosystems for enterprise AI solutions

Take the next step

00 : 00 : 00

Apply to the program now or schedule a call with a program advisor

Unlock exclusive course sneak peek

Application closes: 31st Mar 2026

Talk to our advisor for offers & course details

Admission Process

Admissions close once the required number of participants enroll. Apply early to secure your spot

Fill the application form

Register by completing the online application form.
Application screening

A panel from Great Learning will review your application to determine your fit for the program.
Join the program

Receive an offer for a seat in the upcoming cohort of the program after a final review

Eligibility Criteria

Designed for professionals at different stages of their careers.
Ideal for those looking to advance their knowledge and skills in building Agentic AI systems.

Limited seats available