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Post Graduate Program in AI Agents for Business Applications

Post Graduate Program in AI Agents for Business Applications

Application closes 7th Nov 2026

Why Should You Join This Program?

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    Build Agentic AI Systems with Python

    Progress from Python and LLM foundations to RAG pipelines, single-agent systems, and coordinated multi-agent architectures through hands-on projects.

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    Texas McCombs is a top-ranked US University

    Learn from a top U.S. business school, ranked #1 in Information Systems, #6 for Master’s in Business Analytics, #7 in Artificial Intelligence, and #9 in Business Analytics in the U.S.

LEARNING OUTCOMES

What Will You Learn to Build and Apply?

Through a structured learning journey, you will build the capability to:

  • Navigate the AI landscape and apply foundational concepts to business challenges across industries

  • Use LLMs and prompt engineering to generate reliable outputs, boost performance, and improve prompt stability

  • Implement and evaluate RAG systems using embeddings, vector databases, and external knowledge sources

  • Develop context-aware single-agent systems by integrating tools and memory to automate workflows

  • Apply planning and reasoning strategies that enable agents to decompose tasks and adapt to dynamic scenarios

  • Design and evaluate scalable, transparent multi-agent ecosystems for reliable enterprise AI solutions

Earn a certificate of completion and CEUs from Texas McCombs, a top-ranked U.S. university

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

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

    Eduniversal (2025)

  • #6 Analytics

    #6 Analytics

    U.S. News & World Report (2025)

  • #6 Business Programs

    #6 Business Programs

    U.S. News & World Report (2025)

  • #7 in MS - Business Analytics

    #7 in MS - Business Analytics

    QS World University Rankings (2022)

  • #7 in MS Business Analytics

    #7 in MS Business Analytics

    The Financial Engineer Times (2025)

  • #3 in Information Systems Graduate Programs

    #3 in Information Systems Graduate Programs

    U.S. News & World Report (April 2025)

  • #7 Public University in the U.S.

    #7 Public University in the U.S.

    U.S. News & World Report, 2026

  • #6 in Executive Education - Custom Programs

    #6 in Executive Education - Custom Programs

    Financial Times, 2022

Key program highlights

Why Choose This Program?

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    Learn from Texas McCombs Faculty

    Build core foundations through recorded lectures and monthly faculty-led masterclasses that connect AI concepts to business applications

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    Prefer a No-Code Pathway?

    Connect with a program advisor to learn more about the No-Code Track, implemented through visual, no-code platforms and requiring no prior programming experience.

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    Build Hands-on Expertise to Solve Business Problems

    Create an industry-ready portfolio of hands-on agentic AI projects and case studies that showcase real-world AI mastery

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    Get Personalized Learning Support

    Receive guidance from a dedicated program manager, academic support, discussion forums, peer groups, and the Great Learning community

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    Attend Weekly Live Mentorship Sessions with Industry Experts

    Discuss case studies, see practical demos, clarify doubts, and understand how AI is applied in real-world workflows

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    Earn a Recognized Credential from Texas McCombs

    Earn a Certificate of Completion from the McCombs School of Business at The University of Texas at Austin, upon completion of the program

Skills you will learn

Python

Generative AI

Agentic AI

Large Language Models

Prompt Engineering

Prompt Optimization

Retrieval-Augmented Generation

Embeddings

Vector Databases

LangChain

LangGraph

ReAct

Model Context Protocol

Multi-Agent Systems

Responsible AI

LLM Evaluation

Human-in-the-Loop Evaluation

Python

Generative AI

Agentic AI

Large Language Models

Prompt Engineering

Prompt Optimization

Retrieval-Augmented Generation

Embeddings

Vector Databases

LangChain

LangGraph

ReAct

Model Context Protocol

Multi-Agent Systems

Responsible AI

LLM Evaluation

Human-in-the-Loop Evaluation

view more

  • Overview
  • Learning Journey
  • Curriculum
  • Projects
  • Tools
  • Certificate
  • Faculty
  • Mentors
  • Reviews
  • Fees

Who Is the Program For?

Build the technical depth to design, evaluate, and guide Agentic AI solutions for business applications.

  • Business and Technology Professionals

    Translate business process workflows into agentic workflows and lead their adoption.

  • Data, AI, and Technology Practitioners

    Build intelligent agentic systems that can reason, make decisions, and adapt to dynamic environments.

  • Technology Leaders

    Develop the technical depth to evaluate, govern, and guide the agentic systems their teams build.

How's the Learning Experience of the Program?

Build strategic judgement and human intuition with our unique structured learning approach

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    Learn from Experts

    Learn from Texas McCombs faculty and industry experts to master AI strategy and implementation

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    Learn By Doing

    Work on business problems & case studies using 15+ tools & build an e-portfolio of AI projects

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    Earn a University Credential

    Earn a certificate of completion from Texas McCombs

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    Get Support Throughout the Learning Journey

    A program management team will help you stay on track, and navigate key program milestones

What Will You Learn in the Program?

Designed by Texas McCombs faculty, the curriculum progresses from Python and Generative AI foundations to RAG, single-agent systems, responsible AI, and multi-agent orchestration. Explore prompt optimization, tool integration, memory, system evaluation, and human-in-the-loop validation through structured modules and project work.

  • Hands-on

    Projects

  • 15+

    Tools and techniques

  • Real-world

    Case studies

PRE-WORK

This module introduces learners to the AI landscape. They explore key concepts, technologies, and business applications of AI, Generative AI, and Agentic AI. The module also provides foundational knowledge of the tools and environments used throughout the program. Learners begin building AI-powered solutions using no-code tools.

Concepts Covered

- Introduction to the World of Data and AI - Python Foundations

MODULE 01 | GENERATIVE AND AGENTIC AI FOUNDATIONS

In this module, learners gain an overview of the Generative AI and Agentic AI landscape. They explore the critical distinctions between generative and discriminative AI and their applications. They also gain an understanding of bias and hallucination associated with modern AI systems. Learners then build a strong foundation in how large language models (LLMs) work and in prompt engineering techniques. They explore techniques to improve prompt stability, automate prompt optimization, and establish robust evaluation of Generative AI workflows through deterministic metrics and LLM-as-a-Judge. Finally, learners explore Retrieval-Augmented Generation (RAG), including how to chunk documents, query vector databases, and systematically evaluate retrieval quality to ground models in authoritative external knowledge.

Week 1 | The Generative AI and Agentic AI Landscape

- Differentiate between generative and discriminative AI approaches and explain the working principles of Large Language Models (LLMs) - Evaluate key risks associated with Generative AI, such as bias and hallucination, and identify considerations for responsible use - Assess business applications of Generative AI across areas such as content creation, customer support, and knowledge management - Demonstrate effective interaction with Generative AI and Agentic AI systems (e.g., ChatGPT) to perform tasks such as research and information synthesis

Week 2 | Python Programming with Generative AI

- Leverage Generative AI tools to assist with code generation, explanation, and debugging, improving efficiency in programming tasks - Apply Python data types and variables to represent and manage data within programs - Utilize core Python data structures (lists, tuples, dictionaries) to store, organize, and manipulate collections of data - Develop logical program flows using conditional statements and looping constructs to automate repetitive and decision-based tasks - Design reusable code by creating and using functions to structure and simplify programming solutions

Week 3 | Large Language Models and Prompt Engineering

- Explain the fundamentals of Large Language Models (LLMs) and their underlying mechanisms - Analyze real-world business applications of LLMs to enhance decision-making - Apply prompt engineering strategies to optimize LLM outputs for improved task performance and efficiency

Week 4 | Prompt Optimization and Evaluation

- Evaluate prompt stability by measuring consistency across multiple non-deterministic LLM runs and implementing prompt versioning strategies - Measure the quality of LLM outputs using evaluation metrics such as accuracy, hallucination rates, and confidence scores (e.g., log probabilities) - Apply prompt optimization techniques to improve model performance beyond manual prompting - Design and implement scalable evaluation frameworks by leveraging LLM-as-a-Judge methods and curated gold-standard datasets for reliable human evaluation

Week 5 | Retrieval-Augmented Generation

- Explain the need for external knowledge sources to enhance the accuracy and reliability of Large Language Model outputs - Apply techniques such as data chunking and embeddings to prepare unstructured data for effective retrieval in AI systems - Describe the role of vector databases in enabling semantic search and efficient retrieval of relevant information - Implement and evaluate Retrieval-Augmented Generation (RAG) pipelines to improve factual grounding and performance of AI-powered applications

Week 6 | Learning Break

Week 7 | Project Week

MODULE 02 | BUSINESS APPLICATIONS WITH AGENTIC AI

In this module, learners build on a fundamental understanding of LLMs to explore the design and implementation of AI Agents for automating business workflows. They begin by learning about the foundational components of Agentic AI architectures, including the ReAct (Reasoning and Acting) framework and multi-step reasoning for task decomposition. They also explore practical tool integration patterns and memory incorporation in AI Agents. Learners then examine the fundamental differences between LLMs and classical software security. They ground solutions in the CIA Triad (Confidentiality, Integrity, and Availability) and analyze critical failure modes such as prompt injection attacks. Finally, they learn to implement layered guardrails to architect resilient AI Agents governed by responsible AI principles and safety boundaries.

Week 8 | Single-Agent Systems

- Explain the concept of AI agents and describe their core components, including perception, reasoning, action, and interaction with external environments - Analyze different agentic AI architectures and identify suitable design patterns for building autonomous AI systems - Apply frameworks such as ReAct and relevant agentic AI tools to enable reasoning, tool usage, and task execution in agent workflows - Explain and implement mechanisms for agent memory for context management, including the role of protocols such as Model Context Protocol (MCP) in enabling tool and knowledge integration

Week 9 | Advanced Tools and Planning in AI Agents

- Compare and assess different categories of tools (Python, SQL, Search, APIs) for their effectiveness in handling data-driven and operational tasks - Distinguish between short-term and long-term memory in agents and evaluate their business use cases (e.g., customer support vs. strategic planning) - Implement tool-based agents using MCP to standardize access to external tools and demonstrate their impact on scalability and interoperability - Apply multi-step reasoning and task decomposition to break down real-world workflows into manageable, automatable components

Week 10 | Responsible AI

- Explain the CIA Triad (Confidentiality, Integrity, Availability) and apply it as a framework to assess security risks in LLM-based systems - Identify and analyze common security vulnerabilities in LLM applications, including prompt injection and other adversarial attack vectors - Implement mitigation strategies and guardrail layers to protect LLM systems from misuse, data leakage, and malicious inputs - Design and evaluate secure Generative AI solution architectures while incorporating responsible AI practices and governance checklists

Week 11 | Project Week

MODULE 03 | ADVANCED AGENTIC AI SOLUTIONS

In this module, learners explore how to orchestrate a team of AI Agents for complex business applications. They begin by analyzing the operational bottlenecks of single-agent workflows. They then explore multi-agent orchestration as a scalable coordination solution and Agentic RAG pipelines, where multiple agents collaborate across complex data retrieval and response generation stages. Learners also evaluate AI Agents using metrics such as task completion rate, tool call accuracy, reasoning coherence, and latency. They explore human-in-the-loop validation to ensure accuracy and reliability in Agentic AI solutions.

Week 12 | Multi-Agent Systems

- Analyze the limitations and challenges of single-agent LLM systems in handling complex, multi-step, and collaborative tasks - Explain how multi-agent systems address these challenges by enabling coordination, specialization, and distributed problem-solving - Compare and evaluate common multi-agent architectures and frameworks used to design collaborative agent ecosystems - Design agentic workflows that incorporate techniques such as Agentic RAG to enhance reasoning, knowledge retrieval, and task execution in multi-agent environments

Week 13 | Evaluating Agentic AI Systems

- Evaluate the effectiveness of AI agents using Task Completion Rate to determine how successfully agents achieve their assigned objectives - Assess the correctness of agent interactions with external systems by measuring Tool Call Accuracy - Analyze the quality and logical consistency of agent reasoning using Reasoning Trajectory Coherence, including automated approaches such as LLM-as-a-Judge - Measure agent performance efficiency using metrics such as latency and convergence score to assess speed and resource utilization - Implement Human-in-the-Loop evaluation processes to review and validate agent outputs

Week 14 | Project Week

SELF-PACED COURSES

Attention Mechanism and Transformers

This module provides a comprehensive guide to transformer architectures and their business applications by covering the mechanics of both encoder and decoder components, including embeddings, self-attention, masking, cross-attention, and autoregressive generation, to understand how text is processed and generated. - Attention Mechanism - Self-Attention - Transformer Architecture - Types of Transformer Models (Encoder-Decoder, Decoder-only, Encoder-only)

Fine-tuning Language Models

This module provides a comprehensive overview of the need to fine-tune Large Language Models (LLMs) for specific business use cases, analyzes Parameter-Efficient Fine-Tuning (PEFT) techniques to optimize model performance, and implements QLoRA-based fine-tuning strategies to improve LLM efficiency while minimizing computational costs. - Motivation for Fine-Tuning Language Models - Pretraining versus Fine-Tuning - Fine-Tuning Approaches (Prefix Tuning, Adapter Layers, LoRA, QLoRA) - Data for Fine-Tuning - LLMs versus Small Language Models (SLMs) - Evaluating Domain-Specific Language Models

Claude-based AI Workflows

This module builds learners’ practical capability in applying Generative AI and Agentic AI using the Claude ecosystem. They build the ability to design, execute, and evaluate AI-driven workflows for real-world applications using Claude Chat and Claude CoWork, following a Plan, Approve, Execute, Iterate framework. - Model selection and prompt engineering using Claude Chat - Agentic workflow design and orchestration using Claude CoWork - Plan, Approve, Execute, Iterate framework - Designing workflows with reasoning, tools, and multi-step execution - Applying concepts through real-world case studies *Disclaimer: Claude API access is not included as part of the program. Learners may explore advanced usage independently.

MASTERCLASSES

Multimodal Generative AI Masterclass

This masterclass explores how to solve business problems using Generative AI. Learners examine the capabilities of text-to-image and image-to-text tools. They also explore the speech recognition capabilities of audio-to-text Generative AI tools through business use cases. - Image Generation using GenAI - Audio Generation using GenAI

Anthropic Series Masterclass

This masterclass explores how to design, build, and deploy agentic workflows using Anthropic’s frontier capabilities. Learners begin with prompt engineering for Claude, leveraging system prompts, chain-of-thought, connectors, and thinking modes for deep reasoning. They then explore Claude Cowork for automating complex knowledge work through agentic loops, task oversight, and specialized skills. - Prompt engineering for Claude, system prompts, chain-of-thought, connectors, and thinking modes - Claude Cowork for automating complex knowledge work through agentic loops - Task oversight and specialized skills

Note: The curriculum listed above are indicative and subject to updates as technology evolves.

What Case Studies & Projects Will You Solve?

Work on real-world case studies across industries & functions using 15+ tools and technologies

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Sample Project | Finance

Driving Investment Performance Through Grounded Synthesis

Description

Build a RAG-powered market intelligence assistant that enables brokers to query financial news and regulatory filings in natural language and deliver accurate, source-backed insights that improve investment recommendations.

Skills you will learn

  • Large Language Models
  • Prompt Stability
  • Retrieval-Augmented Generation
  • Embeddings
  • Vector Databases
  • OpenAI
  • LLM Evaluation
  • Financial Document Analysis
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Sample Project | Healthcare

AI-Powered EHR Assistant

Description

Improve patient understanding and reduce support burden by implementing a guardrailed AI assistant that explains EHR information in plain language, answers general medical questions using trusted sources, and enforces strict safety and privacy constraints within the patient portal.

Skills you will learn

  • Single-Agent System
  • Tool Calling
  • ReAct
  • Large Language Models
  • OpenAI
  • LLM Evaluation
  • Responsible AI
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Sample project | logistics and supply chain

AI-Powered Last-Mile Delivery Exception Handling Automation

Description

Improve last-mile logistics efficiency by implementing a multi-agent AI system that detects delivery exceptions from operational logs, applies policy-based reasoning to determine optimal resolution actions, generates personalized customer communications, and escalates complex cases while maintaining full auditability.

Skills you will learn

  • Multi-Agent System
  • Agentic RAG
  • OpenAI
  • Agent Evaluation
  • Human-in-the-Loop Evaluation
  • Workflow Automation
  • Responsible AI
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SAMPLE CASE STUDY | Retail

Retail Order Query Chatbot

Description

Enable dynamic, context-aware interactions that assist customers with product queries and order tracking by developing a retail chatbot using a multi-agent system, improving the overall shopping experience.

Skills you will learn

  • Agentic AI
  • Multi-Agent Systems
  • Chatbot Development
  • Context-Aware Interaction
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Sample Case Study | e-commerce

AI-Powered Customer Feedback Analyzer

Description

Build an AI-powered customer feedback analysis system that delivers grounded, technically detailed review summaries with automated quality evaluation and continuous prompt optimization, enabling Product Managers to identify actionable product improvements, reduce decision-making errors, and enhance customer satisfaction.

Skills you will learn

  • Large Language Models
  • Prompt Optimization
  • Prompt Stability
  • Generative AI Evaluation
  • LLM-as-a-Judge
  • OpenAI
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Sample Case Study | Insurance

Insurance Policy Q&A

Description

Build an AI-powered insurance policy Q&A assistant that delivers accurate, source-grounded answers with exact policy citations across plans and policy years, using continuous evaluation to ensure reliability, safe responses, and auditable compliance while reducing support delays and misrepresentation risks.

Skills you will learn

  • Large Language Models
  • Retrieval Augmented Generation
  • Embeddings
  • Vector Databases
  • Generative AI Evaluation
  • LLM-as-a-Judge
  • OpenAI
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Sample Case Study | Finance

ClaimAudit AI

Description

Build an AI-powered healthcare claims audit assistant that converts natural-language questions into safe, validated SQL queries and delivers accurate, explainable, audit-ready insights in real time, accelerating investigations, strengthening compliance oversight, and reducing reliance on data and IT teams.

Skills you will learn

  • Large Language Models
  • Responsible AI
  • Prompt Injection Mitigation
  • Guardrails
  • Generative AI Evaluation
  • LLM-as-a-Judge
  • OpenAI
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SAMPLE CASE STUDY | Human Resources

Policy Driven HR Assistant

Description

Build an AI-powered HR support agent that retrieves employee-specific data, interprets HR policies, and performs necessary calculations to resolve routine employee queries accurately and reliably, with human escalation for uncertain cases to ensure trustworthy, policy-compliant responses.

Skills you will learn

  • Single-agent System
  • ReAct
  • OpenAI
  • Policy Validation
  • LLM-as-a-Judge
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Sample Case Study | Customer Support

AI Helpdesk Co-Pilot

Description

Improve customer support efficiency by implementing an agentic AI system that classifies support tickets, retrieves relevant knowledge, and generates grounded, policy-compliant first responses with explainable reasoning and automated escalation when needed.

Skills you will learn

  • Multi-agent System
  • Agentic RAG
  • OpenAI
  • Customer Support Efficiency
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SAMPLE CASE STUDY | lOGISTICS

AI-Powered Shipment Disruption Router

Description

Improve logistics operational efficiency by implementing a multi-agent AI system that analyzes shipment disruptions, retrieves policy-based mitigation rules, and generates validated routing decisions with escalation for high-risk cases while maintaining full auditability.

Skills you will learn

  • Improve logistics operational efficiency by implementing a multi-agent AI system that analyzes shipment disruptions
  • retrieves policy-based mitigation rules
  • and generates validated routing decisions with escalation for high-risk cases while maintaining full auditability.
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SAMPLE CASE STUDY | Manufacturing

Feasibility Report Generator

Description

Build an AI-powered manufacturing feasibility assessment system that compares client LVAD requirements against existing manufacturing capabilities and documentation to generate and evaluate structured feasibility reports, identifying capability gaps, required modifications, cost implications, and overall production viability.

Skills you will learn

  • Large Language Models
  • Prompt Engineering
  • LLM-as-a-Judge
  • OpenAI

Note: The projects and case studies listed above are indicative and subject to updates to the curriculum.

Which Tools Will You Learn and Apply?

Learn 15+ tools like OpenAI, Gemini, Hugging Face, Claude & more to build and optimize AI models and workflows

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    Python

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    Google Colab

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    VS Code (Visual Studio Code)

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    OpenAI

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    LangChain

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    ChromaDB

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    FAISS

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    LangChain ReAct

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    LangGraph

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    LangSmith

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    DeepEval

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    DSPy

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    MCP

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    Sentence Transformers

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    ChatGPT

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    Gemini

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    Claude

Note: The tools listed above are indicative and subject to updates as technology evolves.

Earn a Certificate of Completion From Texas McCombs

Stand out in a competitive market with a verified Post Graduate certificate in AI Agents for Business Applications that validates your AI expertise through rigorous, practical assessments

certificate image

* Image for illustration only. Certificate subject to change.

Who Are the Faculty for the Program?

Learn from renowned Texas McCombs faculty and build technical intuition to make credible, strategic decisions

  • Dr. Kumar Muthuraman

    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.

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  • Dr. Daniel A Mitchell

    Dr. Daniel A Mitchell

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

    Ph.D. in Information, Risk, and Operations Management

    15+ years of experience in financial engineering

    Know More
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  • Dr. Abhinanda  Sarkar

    Dr. Abhinanda Sarkar

    Senior Faculty & Director Academics, Great Learning

    30+ years of experience in data science, ML, and analytics.

    Ph.D. from Stanford, taught at MIT, ISI, and IIM Bangalore.

    Know More
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  • Dr. Davood Wadi

    Dr. Davood Wadi

    Supporting Program Faculty

    Assistant Professor, University Canada West (PhD. in Marketing/AI)

    Former Artificial Intelligence Researcher at HEC Montréal

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Who Are the Mentors for Weekly Live Sessions?

Learn from seasoned AI industry mentors in weekly live sessions to apply concepts and build practical skills

  •  Bhaskarjit Sarmah  - Mentor

    Bhaskarjit Sarmah linkin icon

    Head of AI Research, Domyn
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  •  Juan Jose Ortiz  - Mentor

    Juan Jose Ortiz linkin icon

    Managing Director, Data Analytics & AI at Hartman Executive Advisors
    Company Logo
  •  Giancarlo Crocetti  - Mentor

    Giancarlo Crocetti linkin icon

    Director of the St. John’s AI Institute
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  •  Vinicio Desola Jr  - Mentor

    Vinicio Desola Jr linkin icon

    Staff Data Scientist, Newmark
    Company Logo

Note: The mentors listed above are indicative and subject to change based on availability and scheduling.

Course Fees

The course fee is
USD 3,450 USD 3,100

Invest in your career

  • benifits-icon

    14-Week Comprehensive Journey: Build practical skills in Python, Agentic AI, RAG, and multi-agent systems

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    Structured Learning: Learn through recorded faculty lectures and live monthly masterclasses.

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    Hands-On Learning: Apply concepts through hands-on projects and real-world case studies.

  • benifits-icon

    Earn a globally recognized Texas McCombs certificate and to validate your AI expertise

Take the next step

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Learn more about the program

Application Closes: 7th Nov 2026

Application Closes: 7th Nov 2026

Talk to our advisor for offers and course details

Admission Process

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

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    Fill the application form

    Register by completing the online application form.

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    Application screening

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

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    Join the program

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

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