Agentic AI Pioneer Program

Master AI Agents, Build the Future!

  • 150+ Hours of Comprehensive Learning
  • 20+ Hands-on Projects for Skill Building
  • 1:1 Mentorship with Agentic AI Experts
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Become an Agentic AI Expert

How does the Agentic AI program help you?

150+ Hours of Intelligent Agent Training

  • Build AI agents that think, learn, and act autonomously
  • Master advanced Agentic AI frameworks and tools

20+ Real-World Projects

  • Gain hands-on experience with practical simulations
  • Tackle diverse projects to enhance your skills

1:1 Expert Mentorship

  • Receive personalized guidance from industry leaders
  • Accelerate learning with a tailored roadmap to success

Curriculum Statistics

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20+ Projects

Skill building with industry-relevant projects

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150+ Hours

Comprehensive learning to power ahead in your AI journey

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15+ Tools

Master 15+ cutting-edge tools and frameworks

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12+ Assignments

Work on Agentic AI assignments and test your skills

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75+ Mentorship Sessions

1:1 mentorship session with leading AI experts

Personalized Roadmap

Chart your custom learning path, fueled by your ambition and built on your expertise

  • 1Introduction to Generative AI
  • 2Build Your First Agent
  • 3Learn Coding for Agentic AI
  • 4Learn LangChain, Prompt Engineering, RAG
  • 5Build an AI Agent from Scratch
  • 6Build ReAct Agents with LangChain
  • 7Build Your First AI Agent with LangGraph, Autogen, CrewAI
  • 8Learn Agentic AI Architectures & Design Pattern
  • 9Build Advanced AI Agents with LangGraph, Autogen, CrewAI
  • 10Build Agentic RAG Systems with LangGraph
  • 11Build Multi-agent Systems with LangGraph, Autogen, CrewAI
  • 12Build Reflective & Planning Agents with LangGraph, Autogen, CrewAI
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Our Curriculum

Explore 17+ modules, starting from coding essentials for agents and prompt engineering to advanced topics like building automation agents with LangChain, LangGraph, AutoGen, and CrewAI.

20+ Projects
150+ Hours
15+ Tools
15+ Assignments

  1. Understand the Fundamentals of Generative AI

  2. Familiarize yourself with the various components of GenAI universe

  3. Learn Popular Prompt Engineering Techniques

  4. Get to know the basics of Retrival Augmented Generation(RAG) and its applications

  5. Examine the basics of Agents and its applications

  1. Understand AI Agents, their working, and use cases

  2. Identify the potential and features of Agent Developments with Code Free Tools

  3. Build Simple to Advanced AI Agents with NoCode tools

  4. Customize and Deploy AI Agents using NoCode tools

  1. Learn core Python skills for AI programming

  2. Process data from CSV/JSON files using Python

  3. Use SQL and python frameworks to manage databases

  4. Interact with APIs in Python

  5. Prompt LLMs for tasks like summarization, question answering

  6. Build AI apps with frameworks like Flask or FastAPI

  7. Learn about popular tools and frameworks to build AI Agents like CrewAI, AutoGen, LangGraph and LangChain

  1. Learn core LangChain components like LLMs, Model I/O, Parsers, and Chains

  2. Master LCEL for structuring GenAI app pipelines

  3. Practice creating efficient prompt templates and output parsers

  4. Develop simple and complex LLM conversational apps using LangChain

  5. Implement advanced LCEL chains in practical GenAI applications

  1. Learn the core principles of crafting, structuring, and refining effective prompts

  2. Explore various popular prompt engineering patterns including persona, flipped interaction, N-shot and meta language prompting

  3. Gain hands-on experience creating effective prompts for industry-specific use cases like customer service, IT support and more

  4. Learn about the art of conversational prompting

  5. Understand advanced prompting techniques like Chain of Thought, Self Consistency etc. for guiding models to produce multi-step logical outputs

  6. Explore how prompts can interact with external tools for enhanced agentic real-world applications

  1. Learn various document loading and processing techniques to handle PDFs, word documents, and multimodal documents having a mixture of text, images and tables

  2. Explore various document chunking strategies like recursive character, token-based, semantic and agentic chunking to segment large documents

  3. Understand the role of vector databases in RAG systems

  4. Explore various tools for connecting to vector databases like ChromaDB, Weaviate, etc, and performing standard create, read, update, delete (CRUD) operations

  5. Differentiate between other databases like SQL, NoSQL, GraphDB and vector databases

  6. Master simple and complex retrieval strategies including semantic search, hybrid search, multi-query retrieval, context compression and more to retrieve the right context from your vector DB

  7. Learn how to connect Vector DBs to LLMs and build end-to-end RAG Systems

  8. Learn about the most common problems and challenges in RAG Systems and how to fix them

  1. Learn how an AI agent is structured with prompts, tools and LLMs

  2. Build a simple ReAct style AI Agent leveraging Tools and LLMs like GPT-4o

  3. Build an AI Agent based on the Reflection pattern tp generate, reflect and critique, iterate and refine results

  1. Learn how to build a simple ReAct style agent in LangChain with Tool use

  2. Learn how to add conversational memory to this agent

  3. Learn how to extend this agent into real-world scenarios like multi-user conversational memory usage

  1. Learn about the key components of LangGraph

  2. Learn how to architect these components together to build a simple real-world AI Agent

  1. Learn about the key components of AutoGen

  2. Learn how to architect these components together to build a simple real-world AI Agent

  1. Learn about the key components of CrewAI

  2. Learn how to architect these components together to build a simple real-world AI Agent

  1. Learn about what are Agentic AI Design Patterns to architect AI Systems in a systematic way

  2. Learn about the top 4 popular Agentic AI Design Patterns

  3. The Reflection Pattern with examples and real-world applications where it is used

  4. The Tool Use Pattern with examples and real-world applications where it is used

  5. The Planning Pattern with examples and real-world applications where it is used

  6. The Multi-Agent Pattern with examples and real-world applications where it is used

  7. Other patterns and best practice

  1. Learn how to use built-ins for a simple ReAct style agent in LangGraph with Tool use

  2. Learn how to built the same agent from scratch with various LangGraph Components

  3. Learn how to extend the above agent to handle multi-user conversations

  4. Learn how to build a simple reflection agent in LangGraph

  5. Learn how to build a simple multi-agent system in LangGraph

  1. Explore advanced agentic designs with AutoGen

  2. Build industry relevant agentic systems with AutoGen

  3. Construct an agentic system to Execute Code

  4. Learn how to prototype agents using AutoGen Studio

  5. Learn how to extend an agent to handle multi-user conversations

  1. Learn how to build a crew of several agents

  2. Explore advanced agentic designs with crewAI

  3. Build industry relevant agentic systems with crewAI

  4. Learn the frameworks for building multi-agentic system

  1. Learn about popular research on Agentic RAG Systems including Corrective RAG, Self-Reflective RAG, Self-Route RAG

  2. Build an Agentic Corrective RAG System using LangGraph

  3. Extend it to build a Self-Reflective RAG System using LangGraph

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Insights from Industry Leaders on AI Agents

Global Leaders on the Future of Intelligent Autonomous Agents

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“We’ve seen that with the great technological revolutions of the past. Each technological revolution has gotten faster, and this will be the fastest by far. Helpful Agents Are Poised To Become AI’s Killer Function.”

– Sam Altman, CEO of OpenAI

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“AI agents will become our digital assistants, helping us navigate the complexities of the modern world. They will make our lives easier and more efficient.”

– Jeff Bezos, Founder and Executive Chairman of Amazon

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“AI agents will become the primary way we interact with computers in the future. They will be able to understand our needs and preferences, and proactively help us with tasks and decision making.”

– Satya Nadella, CEO of Microsoft

Reinforce your learning with 10+ projects

Projects prepare you for the fast moving industry and give you an edge over others to solve real world problems.

Learning Objective:

  • Build a basic AI agent from scratch
  • Program it to research companies effectively
  • Generate concise, informative company descriptions
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Build a Company Researcher Agent

Learn to create a simple agent that researches a company and creates a short description

Learning Objective:

  • Develop an intelligent agent to review and refine resume content
  • Customize the agent based on your chosen parameters
  • Generate quality improvement suggestions to make your resume stand out
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Create a Resume Reviewer Agent

Create a smart agent to edit and review your resume, offering tailored suggestions

Learning Objective:

  • Develop a chatbot designed to handle customer inquiries
  • Implement functionality to analyze and understand customer problems
  • Deliver accurate and relevant solutions based on the identified issues
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Build a Customer query Chatbot

Build a chatbot to take care of customer queries and provide relevant resolutions based on their problems

Learning Objective:

  • Develop a sophisticated AI agent that compiles comprehensive product information
  • Analyze customer data to identify the most suitable products for individual needs
  • Generate tailored sales pitches to effectively engage potential customers
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Design a Sales Agent

Build an advanced agent that consists of information regarding various products of the company

Learning Objective:

  • Discover the advanced capabilities of GPT-4o for processing multimodal data
  • Analyze handwritten text, graphs, and charts for comprehensive insights
  • Engage with diverse media formats, including images, audio, and video, to enhance understanding and application
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Multimodal Prompt Engineering with GPT-4o

Explores GPT-4o's multimodal capabilities, including handwritten text, graphs, charts, images, etc

Learning Objective:

  • Develop a foundational Retrieval-Augmented Generation (RAG) system
  • Implement source citation for all generated responses
  • Enhance information credibility by linking to original content
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Simple RAG System with Sources

Building a basic Retrieval-Augmented Generation system that cites sources alongside the generated responses

Learning Objective:

  • Design a conversational RAG system that supports multiple users simultaneously
  • Implement memory features to retain context across conversations
  • Ensure the system provides accurate and relevant responses based on user interactions
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Multi-user Conversational RAG System

Developing a conversational RAG system that can handle multiple users and conversations with memory to provide relevant responses

Learning Objective:

  • Integrate various data formats, including text, tables, and images, into a cohesive RAG pipeline
  • Utilize multimodal language models (LLMs) to enhance data processing and analysis
  • Enable the system to answer questions by leveraging diverse multimodal data sources effectively
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Multimodal RAG System

Integrates multiple data formats (text, tables, images) into a RAG pipeline to leverage multimodal LLMs for answering questions

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Meet the instructors & mentors

Our instructor and mentors carry years of experience in data industry

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Qingyun Wu, founder of AG2 (formerly AutoGen) and Assistant Professor at Penn State University, brings over seven years of expertise in AI and machine learning. Her work spans AI agents, reinforcement learning, and algorithm optimization, with roles at Microsoft, Adobe, and Yahoo driving advancements in AI technologies.

Qingyun Wu

Creator and Founder

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Alessandro Romano, Senior Data Scientist at Kuehne+Nagel, has over six years of experience in AI and data science. With roles at FREE NOW and Cargonexx GmbH, he specializes in building AI-driven solutions and AI agents to automate workflows and enhance efficiency. A skilled public speaker, Alessandro effectively bridges technical concepts with diverse audiences.

Alessandro Romano

Senior Data Scientist

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Pio Scelina is an experienced AI Agent developer with over 6 years of expertise in creating advanced AI solutions. His strong research background keeps him at the leading edge of new tools and applications, constantly driving innovation. Known for his passion for exploring emerging technologies and enhancing AI-driven experiences, Pio has established a reputation as a trailblazer in AI-powered transformation.

Pio Scelina

AI Agent Developer

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Kamil Ruczynski is a seasoned AI Agent developer with over 7 years of experience in cutting-edge AI organisations. His deep expertise in research and development enables him to stay ahead of emerging trends and technologies, driving continuous innovation. Renowned for his dedication to pushing the boundaries of AI-driven applications, Kamil has earned recognition as a pioneer in creating transformative AI experiences

Kamil Ruczynski

AI Agent Developer

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Dipanjan has over 10+ years of hands-on and leadership industry experience as well as training, consulting and education initiatives in Data Science and Artificial Intelligence.

Dipanjan S.

Head of Community and Principal AI Scientist

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Bhaskarjit is an award-winning data scientist with a diverse background in multiple domains such as Retail, Airlines, Media & Entertainment, BFSI

Bhaskarjit Sarmah

Vice President

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Kunal has 15+ years of experience in the field of Data Science and is the founder and CEO of Analytics Vidhya- world's 2nd largest Data Science coummunity.

Kunal Jain

Founder & CEO

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Mani Kanteswara Rao Garlapati is an Associate Principal at Google, where he leads data science initiatives focused on fraud and spam detection across various Google products. With a strong background in machine learning and data science, he has previously held positions such as Lead Strategist at Google and Senior Data Scientist at Walmart Labs

Mani Garlapati

Associate Principal

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Lucas Soares is an AI Engineer at Otovo, focusing on AI-driven solutions through large language models (LLMs) and computer vision. With 6+ years of experience across sectors like biometrics and retail, he excels in developing machine learning tools

Lucas Soares

AI Engineer

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Prashant Sahu, an IIT Bombay alumnus and seasoned Corporate Trainer in AI & ML, has over 17 years of diverse experience in areas like research, automation, and cryptography. His expertise extends to developing comprehensive Data Science training materials, including curriculum, case studies, and projects.

Prashant Sahu

Manager - Data Science - Instructor

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Apoorv Vishnoi, a seasoned professional with over 13 years of experience, including more than 10 years in Machine Learning and AI. He holds an MBA from the prestigious Indian School of Business and several certifications in Data Science and Deep Learning. His ability to simplify complex concepts in Data Science and Machine Learning has established him as a respected and influential instructor.

Apoorv Vishnoi

Head - Training Initiative

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Money Back Guarantee!

Agentic AI Pioneer program comes with 7 days no questions asked Money back Guarantee. If the program is bought in pre-launch offer or on discounted price, then the fee paid is non-refundable. For more T&C,Click here

Invest in your future today

  • Build expertise with cutting-edge Agentic AI frameworks
  • Boost Your Career Fast-track your growth with personalized mentorship.
  • Enroll now and start your journey to becoming an Agentic AI expert.
  • Customized Roadmap for Career Success
  • 20+ Projects for Experiential Learning
  • 15+ Cutting edge tools and frameworks

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Frequently Asked Questions

Looking for answers to other questions?

AI agents are autonomous systems designed to sense their environment, process information, and perform actions to achieve specific goals. They function by leveraging AI techniques like machine learning, natural language processing, and decision-making algorithms to automate or assist with tasks. You’ll learn about the role of agents in the AI ecosystem and their real-world applications in the Introduction to Generative AI module.

AI agents are classified into five types based on their complexity and interaction with the environment: Simple Reflex Agents: Follow predefined rules to respond to stimuli. Model-Based Reflex Agents: Use internal models to predict outcomes. Goal-Based Agents: Make decisions aimed at achieving specific objectives. Utility-Based Agents: Evaluate and optimize outcomes for maximum utility. Learning Agents: Improve their performance through experience. These types are discussed in detail in the Agents and Their Applications module.

Yes, ChatGPT is an AI agent specializing in conversational tasks. It uses advanced natural language processing capabilities to understand queries and provide human-like responses, making it a versatile tool for automating communication. The Exploring LLMs module explains how conversational agents like ChatGPT utilize large language models effectively.

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