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Certificate Program in Agentic AI

Certificate Program in Agentic AI

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Application closes 12th Feb 2026

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PROGRAM OUTCOMES

Grow your career with Agentic AI skills

Build agents that perceive, reason, plan, act, and learn with Python and AI

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    Integrate skills into functional agent-based systems to solve real-world problems

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    Design agents using symbolic, BDI, and LLM architectures for specific tasks

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    Address ethical and safety challenges in AI deployment with responsible AI principles

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    Evaluate agent behavior in complex multi-agent and human-agent environments

Earn a Certificate of Completion from Johns Hopkins University

  • #6 National University Rankings

    #6 National University Rankings

    U.S. News & World Report, 2025

  • #13 Best Global University

    #13 Best Global University

    U.S. News & World Report, 2024

  • #1 Computer Information Technology

    #1 Computer Information Technology

    U.S. News & World Report, 2025

  • #1 Biomedical Engineering Program

    #1 Biomedical Engineering Program

    US News and World Report

KEY PROGRAM HIGHLIGHTS

Learn from a top-ranked university

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    Learn from a top-ranked university

    Learn from expert JHU faculty and industry leaders.

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    Industry-focused curriculum

    Gain practical skills in agentic architectures, reasoning models, multi-agent systems, reinforcement learning, and more.

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    Flexible learning format

    Learn from a blend of recorded lectures, live mentored sessions, and AI-assisted learning tools.

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    Live masterclass, hands-on projects

    Work on real-world, hands-on projects to build practical skills in Agentic AI and stand out to employers.

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    Live mentorship by industry experts

    Benefit from professional insights from expert industry practitioners in AI, refine your projects, and set yourself up for success in the field.

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    Dedicated support

    Access a dedicated Program Manager and Academic Learning Support, which includes discussion forums and peer groups for a comprehensive learning experience.

Skills you will learn

Agent Architecture and Design

Reinforcement Learning

Human-Agent Collaboration

Python Programming for Agentic AI

LLM Integration & Prompt Engineering

Agentic AI Frameworks

Agent Architecture and Design

Reinforcement Learning

Human-Agent Collaboration

Python Programming for Agentic AI

LLM Integration & Prompt Engineering

Agentic AI Frameworks

  • Overview
  • Learning Journey
  • Curriculum
  • Tools
  • Certificate
  • Faculty
  • Mentors
  • Fees
  • FAQ
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This program is ideal for

Professionals looking to advance their careers with Agentic AI skills

  • STEM Professionals

    For technical professionals with experience in programming, mathematics, or system design.

  • Data and AI Professionals

    Data Scientists, AI Engineers, and ML practitioners looking to develop autonomous agent systems.

  • Technical Managers and Product Managers

    For leaders aiming to guide intelligent automation and integrate agent-based AI into business workflows.

  • Aspiring Tech Professionals

    Learners without coding backgrounds can start with Python prep modules and advance with structured support.

Experience a unique learning journey

Our pedagogy is designed to ensure a holistic learning experience

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    Learn from world-renowned faculty

    Learn critical concepts through live masterclasses and recorded video lectures by JHU faculty

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    Engage with your mentors

    Clarify your doubts and gain practical skills during weekly live sessions with industry experts

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    Work on hands-on projects

    Work on projects to apply the concepts & tools learnt in the module

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    Get personalized assistance

    Our dedicated program managers will support you through your learning journey

Comprehensive curriculum

The Agentic AI program curriculum, crafted by the expert faculty at Johns Hopkins University and leading industry practitioners, covers everything from Python foundations to advanced AI frameworks, equipping you with the skills to design and deploy autonomous systems.

PRE-WORK: PYTHON REFRESHER FOR AGENTIC AI

This module will equip you with all the tools you need for your learning journey and provide a solid foundation in Python, object-oriented programming, and key data science libraries. ; Pre-Work 1: Landscape of AI, GenAI, and Agentic AI ; Pre-Work 2: Python Basics

MODULE 1: PROMPT ENGINEERING FOUNDATIONS

In this module, you'll get a solid understanding of the fundamentals of Prompt Engineering in Generative AI. You'll learn how Large Language Models (LLMs) operate, uncover the mechanics behind their capabilities, and see how carefully crafted prompts can improve results. This module also introduces Agentic AI, where systems make decisions and take actions based on reasoning similar to humans. By the end, you’ll have practical knowledge of various prompting strategies and how to use retrieval techniques to enhance AI-generated responses.

WEEK 01: PYTHON REFRESHER FOR AGENTIC AI

This week provides a focused refresher on core Python concepts essential for building agentic AI systems, including functions, classes, data structures, and the use of external libraries commonly applied in AI development. ; Python fundamentals Data structures Functions & classes

WEEK 02: INTRODUCTION TO LARGE LANGUAGE MODELS

This week is focused on key concepts of generative AI, how large language models (LLMs) work, what’s under the hood, and why they behave the way they do. The week concludes with an exploration of generative AI’s business applications across industries and functions such as marketing, finance, healthcare, and productivity. The knowledge you gain here is directly applicable to an agentic AI program designed to improve your practical skills. ; LLMs Zero-shot prompting One-shot prompting Few-shot prompting

WEEK 03: PROMPT ENGINEERING TECHNIQUES AND RAG

This week is focused on mastering essential prompt engineering techniques to craft effective natural language solutions. Additionally, learners will explore how retrieval-augmented generation (RAG) enhances large language models (LLMs) by integrating external information, improving the accuracy and relevance of AI-generated responses. ; Chain-of-thought prompting Vector database ; Retrieval-augmented generation (RAG)

WEEK 04: PROJECT 01

Domain: Productivity and Workflow Automation ; Project name: Smart Task Prioritizer Agent About the project: This project focuses on developing an intelligent agent to automate the processing of employee-submitted expense bills. The agent will analyze submissions and generate expense claim documents that are fully compliant with established company expense policies. ; Skills you will learn: Python Programming, Natural Language Processing (NLP), API Integration, Heuristic Decision Making, Command-Line Interface Development, Task List Management, LLM Integration

WEEK 05: LEARNING BREAK

MODULE 2: INTRODUCTION TO AGENTIC AI DESIGN

In this module, you’ll gain a comprehensive understanding of Agentic AI, focusing on its key concepts, distinct characteristics, and practical applications. You’ll learn what defines an agent, how the environment and agent properties influence behavior, and how autonomy, goal complexity, and reasoning interact within intelligent systems. This module blends theory and hands-on learning, helping you assess the differences between traditional AI models and agent-based systems, while also teaching you how to design agents for various real-world scenarios.

WEEK 06: INTRODUCTION TO AGENTIC AI

This week is focused on learning about agentic AI and its key characteristics, distinguishing it from traditional AI paradigms. Learners will develop an understanding of rational agents and their role as foundational models for autonomous decision-making in intelligent systems. They will also gain insight into how agents communicate with one another using the Model Context Protocol (MCP). This week's content gives you an essential foundation in agent design, something you'll encounter in an AI agents course. Agentic AI Agentic workflows Model Context Protocol (MCP)

WEEK 07: CORE AGENT CONCEPTS

This week is focused on the core properties of agents and the types of environments suitable for them. Learners will also compare different types of agents based on their levels of autonomy and the complexity of their goals. This is a great example of the advanced content you’ll find in the agentic AI training online. ; Autonomy Goal-directedness Reactivity Environment types (PEAS) Perceived agency

WEEK 08: ETHICS, SAFETY, ALIGNMENT & RESPONSIBLE AI

This week is focused on examining the AI alignment problem, exploring key ethical challenges and responsible AI principles, analyzing the unique risks posed by agentic AI, and critically assessing agent behaviors through an ethical lens. Key ethical challenges (alignment, safety, responsibility) Responsible AI frameworks Agentic risks

MODULE 3: DESIGNING AND BUILDING AGENTIC SYSTEMS

This module will teach you how to build intelligent agents that can learn from and adapt to their environments. You’ll explore reinforcement learning techniques, multi-agent systems, and embodied AI, giving you a comprehensive understanding of how agent behavior can be optimized. You’ll also learn how to integrate LLMs with agentic frameworks and implement what you've learned through guided case studies and projects, a core part of the Agentic AI program.

WEEK 09: PLANNING & REASONING MECHANISMS

This week is focused on exploring how AI agents plan and reason by examining core paradigms like CoT and ReAct. Learners will learn to trace simple reasoning processes to solve complex tasks using agentic AI and discover how to transform RAG into Agentic RAG. This is essential knowledge for anyone pursuing an agentic AI course online. Planning paradigms (classical, LLM-based) Reasoning (CoT) Reasoning + acting (ReAct) Agentic RAG

WEEK 10: PROJECT 2

Domain: Intelligent Information Retrieval and Knowledge Synthesis ; Project Name: Automated Research Agent ; About the project: This project involves creating an automated research agent designed to retrieve and synthesize information from multiple data sources, including the internet. The agent will provide comprehensive and iteratively refined answers to user queries, enhancing the depth and accuracy of information retrieval. ; Skills you will learn: Python Programming, Web Scraping (BeautifulSoup, requests), API Integration (e.g., NewsAPI), Natural Language Processing (NLP), Text Summarization, Information Retrieval & Structuring, Code Debugging

WEEK 11: LEARNING BREAK

WEEK 12: MULTI-AGENT SYSTEMS (MAS)

This week is focused on core concepts of multi-agent systems (MAS) such as coordination and communication. Learners will analyze strategic interactions through game theory and explore the potential and challenges of integrating large language models (LLMs) into MAS. These are important skills in the Agentic AI training online. ; MAS concepts (coordination, communication) ; Game theory basics ; LLM-based MAS

MODULE 4: ADVANCED AGENTIC AI FRAMEWORKS

This module equips you with ethical and advanced technical frameworks of Agentic AI. You'll explore key challenges around AI alignment, safety, and responsibility, while learning about leading agent development tools and frameworks. The goal is to help you evaluate agent behavior ethically and implement responsible AI principles in your projects. This module culminates in a hands-on project where you'll develop an intelligent customer support chatbot, applying the skills you’ve gained from the agentic AI program.

WEEK 13: REINFORCEMENT LEARNING IN AGENTS

This week is focused on deepening understanding of reinforcement learning by exploring both foundational and advanced concepts, including deep and verbal RL. Learners will evaluate how different learning paradigms support agent adaptability and lifelong learning. ; Reinforcement learning (RL) foundations Advanced RL (deep RL, verbal RL) Lifelong learning

WEEK 14: INTERACTION & EMBODIMENT

This week is focused on exploring key concepts in human-agent interaction such as trust and common ground. Learners will understand the role of simulation in embodied AI and examine the challenges in effective communication and deployment of embodied agents. Human-agent interaction (HAI) (trust, common ground) Embodied AI (simulation, VLA models)

WEEK 15: PROJECT 3

Domain: Customer Support & Automation Project Name: Customer Support Chatbot with Knowledge Base Integration About the project: This project entails the development of a customer support chatbot engineered to address frequently asked questions (FAQs) concerning a specific product or service. The agent will leverage a predefined knowledge base, employ advanced knowledge retrieval techniques, engage in natural language interactions, and integrate a Large Language Model (LLM) to enhance communication clarity and user experience. Skills you will learn: Python Programming, Knowledge Base Management, Natural Language Processing (NLP), Semantic Similarity and Keyword Matching, LLM Integration, Conversational Design, Agent Framework Implementation (LangChain, AutoGen), Retrieval Augmented Generation (RAG), Vector Databases

WEEK 16: THE FUTURE OF AGENTIC AI

This week is focused on helping learners deepen their understanding of agentic AI concepts, applications, and strategic implications for their organizations. The sessions will explore the current limitations of agentic AI, including brittleness, scalability, and memory. They will analyze its role in the pursuit of artificial general intelligence (AGI), identify key research challenges in robust reasoning, continual learning, and autonomous tool use, and evaluate the societal and economic impacts of future agentic technologies.

Learn in-demand tools and techniques

Build a solid foundation in popular/advanced tools and frameworks top employers seek

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    Python on Google Colab and VSCode

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    Vector Databases

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    RAG (Retrieval Augmented Generation)

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    Agentic AI Frameworks

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    LangChain

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    ControlFlow

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    CrewAI

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    LangGraph

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    DSPy

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    AutoGen

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    React

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    Smolagent

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    Chain-of-thought (CoT)

Earn a certificate of completion from Johns Hopkins University

Earn 11 Continuing Education Units (CEUs) upon program completion

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* Image for illustration only. Certificate subject to change.

Meet your faculty

Learn from world-renowned faculty with domain expertise

  • Dr. Shelby Wilson - Faculty Director

    Dr. Shelby Wilson

    Senior Data Scientist - The Johns Hopkins University Applied Physics Laboratory

    Expert in applied mathematics, computational epidemiology, and ML.

    Over a decade of experience solving real-world problems with mathematical and AI tools.

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  • Dr. William Gray-Roncal - Faculty Director

    Dr. William Gray-Roncal

    Principal Research Scientist - Johns Hopkins University Applied Physics Laboratory

    Expert in data science, neuroscience, AI, and precision medicine.

    Leads cutting-edge research in brain network mapping and analysis.

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  • Dr. Iain Cruickshank - Faculty Director

    Dr. Iain Cruickshank

    Faculty Member, Johns Hopkins University

    ML expert applying AI to intelligence, cybersecurity, and social data

    Ph.D, Societal Computing, Carnegie Mellon University School of Computer Science

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  • Dr. Ian McCulloh - Faculty Director

    Dr. Ian McCulloh

    Manager, AI Continuing and Exec Ed, Johns Hopkins University

    Served as Chief Data Scientist and MD of AI at Accenture Federal Services

    Author of three books and over 100 peer-reviewed papers

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Interact with our industry mentors

Interact with dedicated mentors who are current practitioners and experts in Agentic AI

  • Tanya Glozman - Mentor

    Tanya Glozman linkin icon

    Applied Science - AI/ML, Apple
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  • Bridget Huang-Gregor - Mentor

    Bridget Huang-Gregor linkin icon

    Tech Lead Engineering , Capital One
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  • Kalle Bylin - Mentor

    Kalle Bylin linkin icon

    Product Engineer, Workday
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  • Vinicio Desola Jr - Mentor

    Vinicio Desola Jr

    Senior AI Engineer Newmark
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Course Fees

The course fee is USD 3,000

Invest in your career

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    Learn Agentic AI design and deployment

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    Build job-ready skills in agent-based AI

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    Create an industry-ready portfolio of work

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    Earn a certificate of completion from Johns Hopkins University and stand out to employers

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Easy payment plans

Avail our EMI options & get financial assistance

  • INSTALLMENT PLANS

    Upto 12 months Installment plans

    Explore our flexible payment plans

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  • discount available

    Upfront Discount: USD 3,000 USD 2,850

    Referral Discount: USD 3,000 USD 2,850

Third Party Credit Facilitators

Check out different payment options with third party credit facility providers

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*Subject to third party credit facility provider approval based on applicable regions & eligibility

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

Learn more about the program

Application Closes: 12th Feb 2026

Apply Now

Application Closes: 12th Feb 2026

Talk to our advisor for offers & course details

Schedule a call

Application process

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

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    Fill application form

    Apply by filling out a simple online application form.

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    Review process

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

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    Join program

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

Course Eligibility

  • A STEM background with some prior familiarity with a programming language or technical subject matter in maths / data etc. is recommended.

Limited seats available

Apply now and advance your career with cutting-edge Agentic AI skills

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Delivered in Collaboration with:

Johns Hopkins University is collaborating with online education provider Great Learning to offer the Certificate Program in Agentic AI. Great Learning is a professional learning company with a global footprint in 14+ countries. Its mission is to make professionals around the globe proficient and future-ready. This program leverages JHU's leadership in innovation, science, engineering, and technical disciplines developed over years of research, teaching, and practice. Great Learning manages the enrollments and provides industry experts, student counselors, course support and guidance to ensure students get hands-on training and live personalized mentorship on the application of concepts taught by the JHU faculty.

Certificate Program in Agentic AI

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phone icon Application Closes 12th Feb 2026

Got more questions? Talk to us

Connect with our advisors and get your queries resolved

Speak with our expert +1 410 632 6604 or email to jhu-agentic-ai@mygreatlearning.com

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Frequently asked questions

Program Details
Fee and Payment
Admissions and Eligibility
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What is unique about Johns Hopkins University’s Certificate Program in Agentic AI?

The Certificate Program in Agentic AI is a 16-week online program offered by Johns Hopkins University (JHU). Key program highlights include: 


  • Hands-on projects using Python, OpenAI LLMs, and advanced AI frameworks like Reinforcement Learning and Multi-Agent Systems. 
  • Monthly live masterclasses by JHU faculty and 15 live sessions with industry experts for personalized mentorship. 
  • A flexible online format that allows working professionals to learn without disrupting their careers. 
  • Get personalized assistance from a dedicated Program Manager and academic support through the Great Learning community, project discussion forums, and peer groups

How will my performance be assessed in the Agentic AI program?

Your performance in this Agentic AI program will be assessed based on:

  • Hands-on projects that involve building autonomous AI agents using Python and relevant AI libraries.
  • Active participation in live sessions and peer discussionsto demonstrate your engagement and collaborative learning.

Is the Agentic AI program online?

Yes, the Certificate Program in Agentic AI offered by Johns Hopkins University is delivered fully online, offering flexibility for professionals to learn while balancing their work commitments. It combines recorded video lectures, live faculty-led masterclasses, live mentorship sessions by global industry experts, and hands-on projects to ensure an engaging and comprehensive learning experience.

Will I work on hands-on projects in the JHU Agentic AI program?

Yes, you will get to work on multiple hands-on projects in this Certificate Program in Agentic AI, such as:

  • Smart Data Processing Agent: Automating the processing of employee-submitted expense bills. 
  • Automated Research Agent: Creating an agent that synthesizes information from multiple data sources. 
  • Customer Support Chatbot: Developing a chatbot for customer support with knowledge base integration. These projects allow you to apply your learning to real-world challenges.

Will I receive a certificate upon completing the program?

Yes, upon successful completion of the Certificate Program in Agentic AI, you will receive a Certificate of Completion from Johns Hopkins University, along with 11 Continuing Education Units (CEUs), and a shareable e-Portfolio showcasing your skills.

Who are the mentors for this program?

The industry mentors for this Certificate Program in Agentic AI come from leading companies in AI and technology, including: 


  • Apple 
  • BlackRock 
  • Workday 
  • Newmark 
  • Capital One 


These mentors bring practical insights and guidance based on their experiences in the AI field.

What are the rankings of Johns Hopkins University?

Johns Hopkins University (JHU) is consistently ranked among the top 10 universities in the U.S. and is widely recognized for its excellence in research, innovation, and academic leadership.

According to the U.S. News & World Report 2025 rankings, JHU is:

  • #6 among National Universities 
  • #1 in Computer Information Technology 
  • #13 among Best Global Universities

What is the curriculum for JHU’s Certificate Program in Agentic AI?

The curriculum for the Certificate Program in Agentic AI is designed and taught by best-in-class faculty at Johns Hopkins University and leading industry practitioners. The objective of the program is to equip learners with the skills needed to solve problems and deploy Agentic AI solutions across various business applications through a range of topics, including: 


  • Pre Work 1: Landscape of AI, GenAI, and Agentic AI 
  • Pre Work 2: Python Basics 
  • Module 1: Prompt Engineering Foundations 
  • Module 2: Introduction to Agentic AI Design 
  • Module 3: Designing and Building Agentic Systems 
  • Module 4: Advanced Agentic AI 


The curriculum focuses on both theory and practical application, providing a holistic approach to learning Agentic AI.

Who will be the faculty for this Agentic AI program?

The program is taught by distinguished faculty members from Johns Hopkins University, including 


  • Dr. Shelby Wilson: Senior Data Scientist - The Johns Hopkins University Applied Physics Laboratory 
  • Dr. William Gray-Roncal: Principal Research Scientist - Johns Hopkins University Applied Physics Laboratory 
  • Dr. Ian McCulloh: Faculty Member, Johns Hopkins University 
  • Dr. Pedro Rodriguez: Lead - Information Science Branch at Johns Hopkins Applied Physics Laboratory 
  • Dr. Iain Cruickshank: Faculty Member, Johns Hopkins University 

These faculty members bring extensive academic and industry experience to the program.

What key tools and techniques will I learn in this program?

This Agentic AI program covers tools and techniques like 


  • Python 
  • Google Colab 
  • VS Code 
  • Vector Database (Chroma/Pinecone) 
  • LangGraph 
  • LangChain 
  • RAG (Retrieval Augmented Generation) 
  • DSPy 
  • OpenAI 
  • Autogen 
  • Smolagents 
  • CrewAI

How will Johns Hopkins University’s Certificate Program in Agentic AI help me progress in my career?

This Agentic AI program will help you progress in your career in the following ways:

  • Develop a deep understanding of Agentic AI, enabling you to design systems that autonomously make decisions and adapt to new information.
  • Gain practical experience with tools like OpenAI LLMs, Python, and reinforcement learning, making you highly competitive in AI-related roles.

What is the program fee?

The fee for the Agentic AI program is $3,000. For information on offers, payment plans, and eligibility for financial assistance, please reach out to the Program Advisor at Great Learning.

Is the fee refundable?

The program fee is generally non-refundable. Please contact the Program Advisor for specific cancellation and refund policies.

Who is this Agentic AI program for?

The Certificate Program in Agentic AI is designed for individuals aiming to develop and deploy AI systems that can autonomously perform tasks, make decisions, and adapt to dynamic environments. It is ideally suited for:

  •  STEM (Science, Technology, Engineering, Mathematics) Professionals: Technical professionals with experience in programming, mathematics, or system design. 

  • Data and AI Professionals: Data Scientists, AI Engineers, and ML practitioners looking to develop autonomous agent systems. 
  • Technical Managers and Product Managers: Leaders aiming to guide intelligent automation and integrate agent-based AI into business workflows. 
  • New Entrants to Technology: Learners without coding backgrounds can start with Python prep modules and advance with structured support.

Do I need prior experience in AI or programming to enroll in this program?

No, while prior experience is beneficial, the program includes a Python prework module to ensure beginners can build a strong foundation in the required skills.

What are the prerequisites for this program?

  • The ideal candidate should have a foundational understanding of programming languages or core technical concepts in mathematics, data science, or related disciplines. 
  • Beginners are encouraged to complete the Python Prework Module offered as part of the program to build essential skills and ensure a smooth learning experience.

Is there a deadline for the application?

The program follows a rolling admission process and will close once the required number of candidates have been enrolled. Apply early to secure your spot.

Is financial assistance available?

Yes, there are options for financial assistance and payment plans. Please contact the Program Advisor at Great Learning for more details.

What is Agentic AI? How does it differ from traditional AI?

Agentic AI refers to intelligent systems that can: 


  • Make autonomous decisions, reason, plan, and act independently, without constant human oversight. 
  • Adapt to new data and environments while pursuing defined goals. 
  • Unlike traditional AI, which follows pre-programmed instructions, Agentic AI systems can initiate actions and manage tasks autonomously.

How does the program incorporate Large Language Models (LLMs)?

The program offers hands-on experience with OpenAI LLMs. You will learn how to integrate these models into Agentic AI systems for tasks like reasoning, planning, and human-agent collaboration.

How does Agentic AI work?

Agentic AI operates by utilizing intelligent agents that can perceive their environment, reason through problems, plan actions to achieve goals, and act accordingly based on these plans. These agents are designed to function autonomously, adapting their behavior to dynamic situations using various techniques, such as Reinforcement Learning and Multi-Agent Systems.

What are the main applications of Agentic AI?

Agentic AI is applied in various industries, including:


  • Finance for automated decision-making and risk management. 
  • Retail for personalized customer experiences and autonomous shopping assistants. 
  • Healthcare for autonomous diagnosis and personalized treatment plans. 
  • Supply Chain management to automate complex logistics and decision-making.

What are the challenges of developing Agentic AI systems?

Some challenges of Agentic AI development include: 


  • Ensuring ethical decision-making and avoiding biased or unsafe behavior. 
  • Addressing the alignment problem, where AI’s objectives might diverge from human goals. 
  • Managing the complexity of integrating autonomous systems with existing workflows and systems.