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Certificate Program in Agentic AI

Certificate Program in Agentic AI

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Application closes 16th Apr 2026

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PROGRAM OUTCOMES

Grow your career with Agentic AI skills

Learn to build Python-based AI agents that reason, plan, act, and learn autonomously

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    Understand AI evolution from ML to Agentic AI and evaluate real-world business impact

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    Write production-ready Python for AI systems with modular, efficient logic

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    Learn to build AI apps using LLMs, prompt engineering, and AI-assisted coding workflows

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    Design RAG systems with vector databases and optimize retrieval for accuracy

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    Develop and evaluate agentic systems using ReAct, MCP, and multi-agent architectures

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    Deploy, monitor, and secure AI systems with evaluation, observability, and Responsible AI principles

Earn a Certificate of Completion from Johns Hopkins University

  • #7 National University Rankings

    #7 National University Rankings

    U.S. News & World Report, 2026

  • #2 Computer Information Technology

    #2 Computer Information Technology

    U.S. News & World Report, 2026

  • #14 Best Global University

    #14 Best Global University

    U.S. News & World Report, 2026

  • #1 Biomedical Engineering Program

    #1 Biomedical Engineering Program

    US News and World Report, 2026

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
  • Projects
  • 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.

  • Practice With

    OpenAI API

  • 13+

    Reall-World Case Studies

  • Masterclass on

    Anthropic Series

Pre Work

This pre-work module introduces learners to the evolution of AI, from ML and DL to NLP, Generative AI, and Agentic AI, and explores their business impact. It also covers foundational Python programming, including variables, loops, conditionals, and modular functions, providing essential coding skills for AI development.

Landscape of AI, Gen AI, and Agentic AI

Trace the evolution of AI from Machine Learning and Deep Learning to NLP, Generative AI, and Agentic AI. Evaluate the business impact of these technologies by analyzing their real-world applications and strategic use across industries.

Python for Agentic AI

Understand foundational Python programming constructs for AI development, including variables, data types, loops, and conditional statements. Write modular functions and efficient control flows to establish the coding foundation needed to design and implement advanced Agentic AI systems.

Module 01 | Generative AI Foundations

This module introduces learners to setting up Python environments, using key Data Science libraries, and applying “vibe coding” for rapid AI prototyping. Learners will explore LLM mechanics, Prompt Engineering techniques, and Retrieval Augmented Generation (RAG) with vector databases. The module also covers prompt optimization and hybrid evaluation frameworks to ensure reliable, high quality AI outputs.

Week 1: Python and Vibe Coding for Agentic AI

Set up your Python environment and work with key data science libraries essential for building AI systems. Explore “vibe coding” using tools like Cursor, Codex, or Claude to rapidly prototype and solve practical problems, establishing a strong, modern programming foundation for your agentic AI journey.

Week 2: LLMs and Prompt Engineering

Explore the core mechanics of Large Language Models (LLMs) and analyze practical examples of their functionality. Define prompt engineering and differentiate between key techniques such as zero-shot, few-shot, and chain-of-thought prompting to effectively guide AI behavior in real-world applications.

Week 3: Retrieval-Augmented Generation

Master the principles of Retrieval-Augmented Generation (RAG) to build AI systems grounded in external data. Explore advanced optimization techniques for efficient information retrieval and work with vector databases.

Week 4: Prompt Optimization and Evaluation

Transition from manual prompt engineering to programmatic optimization using frameworks like DSPy, while learning to select the right models for diverse NLP tasks. Design scalable, hybrid evaluation systems using LLM-as-a-judge methods and human-validated datasets to ensure prompt stability. Leverage frameworks like RAGAS and DeepEval to measure accuracy, reduce hallucinations, and deliver robust, high-quality AI outputs.

Week 5: Hands-On Project

Learn how RAG integrates financial data with qualitative AI insights using industry relevant tools and technologies.

Week 06 | Learning Break

Learning breaks are structured pauses that allow you to consolidate concepts, complete pending work, and reinforce your understanding before progressing further.

Module 02 | Introduction to Agentic AI Design

This module guides learners through designing and deploying autonomous single-agent systems using the ReAct framework and MCP, integrating external tools. It also covers AI alignment and Responsible AI principles, analyzing agent risks and applying neuro-symbolic methods to ensure safe, ethical, and robust AI deployment.

Week 7: Core Concepts of Agentic AI Systems

Dive into the core mechanisms of autonomous agents, including reasoning, planning, and tool use. Differentiate between key AI memory types and categorize agent architectures based on their capabilities. Apply this knowledge to build complex, context-aware retrieval systems using Agentic RAG.

Week 8: Planning & Reasoning Mechanisms

Apply the ReAct framework and Model Context Protocol (MCP) to enhance agent reasoning and enable seamless integration of external tools. Design and deploy autonomous single-agent architectures for end-to-end task execution, and evaluate agent performance using established metrics and LLM-as-a-judge methodologies.

Week 9: Ethics, Safety, Alignment & Responsible AI

Explore the AI alignment problem and its role in building safe systems, while integrating core responsible AI principles including fairness, transparency, accountability, safety, and privacy. Analyze behavioral risks in autonomous agents, including specification gaming, reward hacking, and unintended side effects. Apply neuro-symbolic AI approaches to design deterministic guardrails that support the safe, ethical, and aligned deployment of advanced AI systems.

Week 10: Project Week

Learn how LangGraph and RAG can be combined to analyze entities, track performance, assess sentiment, and generate actionable recommendations with clear source attribution.

Weel 11 | Learning Break

Learning breaks are structured pauses that allow you to consolidate concepts, complete pending work, and reinforce your understanding before progressing further.

Module 03 | Designing and Building Advanced Agentic AI Systems

In this module, learners dive into Multi-Agent Systems, human-agent interaction, and production-grade agent deployment. They will evaluate agent performance using DeepEval, implement robust Agent-to-Agent communication, and apply neuro-symbolic AI for deterministic validation. The module also covers full observability, Zero-Trust security, and scaling agents to production with containerization and CI/CD workflows.

Week 12: Multi-Agent Systems (MAS)

Explore the fundamentals of Multi-Agent Systems (MAS) and the coordination challenges that arise when scaling beyond single-agent setups. Compare key architectural models, including hierarchical and conversational structures, and analyze the mechanics of communication, coordination, and agent interaction. Implement Agent-to-Agent (A2A) communication protocols for seamless data exchange and design collaborative multi-agent frameworks, such as writer–critic systems. Integrate Small Language Models (SLMs) for specialized sub-agents to improve processing efficiency and optimize system costs.

Week 13: Interaction & Embodiment (HITL)

Explore key concepts in human-agent interaction, including trust and common ground. Understand the role of simulation in embodied AI and examine the challenges of effective communication and deployment of embodied agents.

Week 14: Evaluation of Agentic AI Systems

This week, you will evaluate agentic AI across key dimensions like task success, reasoning trajectories, and system efficiency using frameworks like DeepEval. You will apply diverse methodologies, combining LLM-as-a-Judge with Human-in-the-Loop (HITL) reviews. Finally, you will utilize neuro-symbolic AI to transition from probabilistic assessments to robust, deterministic, rules-based judges for rigorous system validation.

Week 15: Monitoring and Observability - Tracing, Logging, Feedback

Establish full observability in production workflows through comprehensive logging and tracing. Build real-time dashboards to monitor agent latency, costs, and failure rates. Implement continuous feedback loops and use data-driven debugging to identify misbehavior and optimize prompts, tools, and models.

Week 16: Securing Agentic AI Systems

Explore the paradigm shift toward autonomous agentic AI and its implications for system security. Analyze critical threat vectors, including vulnerabilities such as agent goal hijacking and tool misuse caused by manipulated objectives. Apply input sanitization and least-privilege principles to treat all natural language inputs as untrusted. Architect resilient systems using zero-trust principles, incorporating human-in-the-loop (HITL) validation for high-impact actions and ensuring observability through tamper-proof logs.

Week 17: Pre-Deployment and Operationalization of Agentic System

Transition AI agents from notebook POCs to production-ready systems. Containerize applications and implement end-to-end CI/CD workflows for scalable, seamless delivery. Enforce safe and reliable deployments through rigorous testing, full observability, and fault-tolerant rollout strategies for managing autonomous systems at scale.

Week 18: Project Week

Learn how a multi-agent AI system can standardize decision-making using LangGraph, RAG, and deterministic tools, ensuring compliance, bias checks, and human-in-the-loop oversight.

Self-Paced Module | Reinforcement Learning

In this self-paced module, learners deepen their understanding of reinforcement learning. exploring foundational and advanced concepts while evaluating how different paradigms enhance agent adaptability and lifelong learning.

Masterclass | Anthropic Series

This masterclass covers the Anthropic AI landscape, exploring Claude models, Constitutional AI, and key safety and alignment principles. Learners will apply effective prompting, use the Claude API for tasks and integrations, generate structured outputs, build simple applications, critically compare Claude with other AI models, and evaluate ethical considerations for deploying AI systems.

Sample Case Studies

Apply your learning through real-world case studies guided by global industry experts.

"Fridge Clear-Out" Assistant

CONSUMER TECH Understand how Python and data structures minimize food waste by matching ingredients to recipes. Learn fuzzy logic, set operations, and conditional filtering to generate actionable recommendations. Skills You Will Learn: Python, Data Structures, Fuzzy Logic

Prompt Engineering Fundamentals

NLP Learn how prompt engineering applies to diverse tasks like text summarization, sentiment analysis, quizzes, presentations, and personalized responses. Understand prompt design and optimization techniques for real-world use. Skills You Will Learn: Prompt Engineering, NLP, Text Processing

RAG Notebook: AppleHBR Report Q&A

DATA SCEINCE Understand how RAG enables answering questions from PDFs using structured retrieval. Learn to combine document embeddings with LLMs for accurate, context-aware insights. Skills You Will Learn: RAG, NLP, Information Retrieval

RAG with DSPy: AppleHBR Report & RAGAS

AI EVALUATION Learn to evaluate RAG-based systems using DSPy and RAGAS frameworks. Understand metrics for accuracy, relevance, and hallucination reduction in knowledge-grounded AI workflows. Skills You Will Learn: RAG, AI Evaluation, LLM Assessment

Claims Processing for Auto Insurance – AgenticRAG

INSURANCE Learn how Agentic RAG and SmolAgents automate claims by parsing data, retrieving policies, and reasoning over rules to generate structured decisions and payout recommendations. Skills You Will Learn: Agentic AI, RAG, Insurance Analytics

AI Legal Research & Analysis Agent

LEGAL TECH Understand how AI agents combine RAG, real-time search, and structured reasoning to deliver reliable legal insights. Learn evaluation methods using DeepEval for tool correctness and answer relevance. Skills You Will Learn: RAG, Legal AI, System Evaluation

Responsible AI Chat Agent

E-COMMERCE Learn how multi-agent systems autonomously handle customer queries while enforcing Responsible AI safeguards. Explore prompt injection defense, PII masking, and ethical tool integration. Skills You Will Learn: Multi-Agent Systems, Responsible AI, Customer Support Automation

AI Researcher Multi-Agent System

RESEARCH Understand how multi-agent systems using LangGraph track, evaluate, and synthesize AGI research. Learn metrics for relevance, novelty, methodology, and impact to highlight trends and gaps. Skills You Will Learn: Multi-Agent Systems, Research Analysis, LLMs

Healthcare Intelligence Assistant: Natural Language SQL

HEALTHCARE Learn how AI enables natural-language-to-SQL queries with Human-in-the-Loop safety, audit logging, and compliance controls for responsible healthcare applications. Skills You Will Learn: NLP, HITL, Healthcare AI

AI Researcher Multi-Agent System: Multi-Layer Evaluation

RESEARCH Understand multi-agent evaluation using DeepEval across reasoning, action, and execution. Learn LLM-as-a-Judge and neuro-symbolic methods to enforce deterministic validation. Skills You Will Learn: Multi-Agent Systems, Evaluation, Neuro-Symbolic AI

Autonomous IT Helpdesk Agent: Full-Stack Observability

IT Learn how LangGraph-based helpdesk agents monitor reasoning and tool usage in production. Explore dashboards, trace analysis, and feedback loops for continuous agent optimization. Skills You Will Learn: Observability, Multi-Agent Systems, IT Automation

Secure Financial Compliance Agent: Zero-Trust Architecture

FINANCE Understand autonomous compliance agents with threat mitigation, input sanitation, and Zero-Trust frameworks. Learn to prevent prompt injection, tool misuse, and agent goal hijacking. Skills You Will Learn: Agentic AI Security, Responsible AI, Zero-Trust

Autonomous Warehouse Navigation (PPO)

LOGISTICS Learn how Reinforcement Learning and PPO enable agents to navigate dynamic warehouse layouts. Explore reward shaping, path optimization, and performance evaluation across scenarios. Skills You Will Learn: Reinforcement Learning, PPO, Autonomous Navigation

Work on hands-on projects and case studies

Engage in hands-on projects and real-world case studies using emerging tools and technologies

  • 30+

    Tools and Techniques

  • 18+

    Live Mentorship Sessions

  • 3

    Hands-On Projects

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Finance

DualLens Analytics: Financial Insight with AI Intelligence

Description

Learn how RAG integrates financial data with qualitative AI insights to evaluate organizational performance. Understand how combining structured metrics and AI signals supports more informed investment decisions.

Skills you will learn

  • RAG
  • Financial Analysis
  • Data Integration
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Finance

Autonomous Financial Research Analyst

Description

Understand how LangGraph and RAG combine to analyze AI companies, track performance, assess sentiment, and provide actionable investment recommendations with clear sourcing.

Skills you will learn

  • LangGraph
  • RAG
  • Financial Analysis
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Finance

Senior Mortgage Underwriting System

Description

Learn how a multi-agent AI system standardizes mortgage decisions using LangGraph, RAG, and deterministic tools, ensuring compliance, bias checks, and human-in-the-loop oversight.

Skills you will learn

  • Multi-Agent Systems
  • RAG
  • Risk Analysis

30+ in-demand tools and techniques

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

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    Python

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    ChatGPT

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    LangChain

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    ChromaDB

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    RAG (Retrieval Augmented Generation)

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    DSPy

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    LangGraph

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    MCP

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    React

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    CrewAI

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    AutoGen

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    LangSmith

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    Docker

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    Github

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    AWS

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    Claude Code

Earn a certificate of completion from Johns Hopkins University

Earn 11 Continuing Education Units (CEUs) upon program completion

certificate image

* 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.

    Know More
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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.

    Know More
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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

    Know More
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  • Dr. Ian McCulloh - Faculty Director

    Dr. Ian McCulloh

    Director of AI Executive & Professional Education, Johns Hopkins University

    Served as Chief Data Scientist and MD, AI, Accenture Federal Services

    Author of three books and over 100 peer-reviewed papers

    Know More
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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
    Apple Logo
  • Bridget Huang-Gregor - Mentor

    Bridget Huang-Gregor linkin icon

    Tech Lead Engineering, Capital One
    Company Logo
  • Kalle Bylin - Mentor

    Kalle Bylin linkin icon

    Product Engineer, Workday
    Company Logo
  • Vinicio Desola Jr - Mentor

    Vinicio Desola Jr

    Senior AI Engineer Newmark
    Newmark Logo
  • Bhaskarjit Sarmah - Mentor

    Bhaskarjit Sarmah linkin icon

    Head of AI Research, Domyn
    Company Logo

Course Fees

The course fee is USD 3,450

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

    View Plans

  • discount available

    Scholarship: USD 3,450 USD 3,250

    One Time Discount: USD 3,450 USD 3,275

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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Learn more about the program

Application Closes: 16th Apr 2026

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Application Closes: 16th Apr 2026

Talk to our advisor for offers & course details

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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

Apply Now

Batch start date

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 16th Apr 2026

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Frequently asked questions

Program Details
Fee and Payment
Admissions and Eligibility
Others
Program Details

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.

Fee and Payment

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.

Admissions and Eligibility

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.

Others

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.