Real World Projects on AI Agents

  • BeginnerLevel

  • 144+Students Enrolled

  • 5 Hrs Duration

  • 4.7Average Rating

About this Course

  • Learn how to design, build, and orchestrate AI agents through five capstone projects covering research, analytics, databases, content automation, and HR workflows.
  • Build end-to-end agentic systems using tools like CrewAI, planners, tools, memory, and evaluators while solving real business problems with structured workflows.
  • Gain hands-on experience in data extraction, reasoning, analysis, report generation, and decision-making using multi-step, tool-driven AI agents.
  • Create production-style AI agents that showcase real-world applications, helping you build a strong portfolio aligned with modern AI and agentic roles.

Learning Outcomes

Newsletter AI Agent Build

Automate content discovery, filtering, and summaries

Deep Research Agent

Perform multi-steps research and insight synthesis

Learn Data Analyst Agent

Analyze data, generate insights, and explain results

Database Agent Develop

Query databases and reason over structured data analyse

Who Should Enroll

  • Aspiring AI, GenAI, and agentic AI professionals looking to build real-world, portfolio-ready agent projects.
  • Data scientists, ML engineers, and developers aiming to transition from prompts to full agentic system design.
  • Product, analytics, and automation professionals seeking hands-on experience with practical AI agent workflows.

Course Curriculum

Design and build five real world AI agent projects covering newsletter automation, deep research, data analysis, database interaction, and multi-agent resume review systems using real-world workflows.

toolstoolstools

  1. 1. Introduction to the Course

  2. 2. Meet the Instructor

  3. 3. Let's learn AI2l Labs Maestro

  4. 4. Hands on: AI21 Studio Part 1

  5. 5. Hands on: AI21 Studio Part 2

  6. 6. Hands on: AI21 Studio Part 3

  1. 1. Project Introduction & Architecture

  2. 2. Project Implementation - Project Setup -Part-1

  3. 3. Project Implementation - Part II - Create Utility Functions

  4. 4. Project Implementation -Part III - Create Report Planner Node

  5. 5. Project Implementation Part- IV - Create Section Builder Sub-Agent

  6. 6. Project Implementation - Part V - Create Format Sections Node

  7. 7. Project Implementation - Part VI - Create Final Section Writer Node

  8. 8. Project Implementation - Part VII - Create Compile Final Report Node

  9. 9. Project Implementation Part VIII - Create Planning Agent

  10. 10. Reading Resources

  11. 11. Handouts of the course

  1. 1. Introduction to AI

  2. 2. Introduction to AI Agents

  3. 3. AI Agents design patterns

  4. 4. Data Analyst agent with CrewAI

  5. 5. Hands On: Data Analyst agent with CrewAI

  1. 1. Introduction to Database Agents with LLMs

  2. 2. Building and Enhancing the Database Agent

  1. 1. Introduction to Problem Statement

  2. 2. Introduction to CrewAI

  3. 3. Core Components of CrewAI

  4. 4. Hands on: Building a Resume Review Agectic System with CrewAI

Meet the instructor

Our instructor and mentors carry years of experience in data industry

company logo
Dipanjan Sarkar

Principal AI Scientist

Dipanjan Sarkar, Head of AI, Author & Consultant, has 12+ years of expertise in ML, DL, GenAI, CV & NLP. He has led AI initiatives across Fortune 100 firms & startups, building data products & upskilling professionals at all levels.

company logo
Shikha

Senior Manager - Data Science

Shikha has 12+ years in Data Science, building scalable AI in marketing, recommender systems & NLP. Recent focus: Generative AI, LLMs, RAG, fine-tuning, and deploying end-to-end applications at scale.

company logo
Srimugunthan Dhandapani

Technical Lead, WellsFargo

Srimugunthan brings over 12 years of experience in data analytics, specializing in prediction models, recommender systems, and more. With a background in computer science and an Executive MBA in product management.

company logo
Apoorv Vishnoi

Head of Training

Apoorv is a seasoned AI professional with over 14 years of experience. He has founded companies, worked at start-ups and mentored start-ups at incubation cells.

company logo
Chen Wang

Lead Architect, AI21 Labs

Chen Wang, Lead Alliance Solution Architect at AI21 Labs, is a former Lead Data Scientist at IBM and Director at DataRobot. With a Ph.D. from NYU, he brings deep AI expertise and real-world experience to this course.

Get this Course Now

With this course you’ll get

  • 5 Hours

    Duration

  • Beginner

    Level

Certificate of completion

Complete the course and pass the final assessment to receive an industry-recognized certificate

  • Industry-Recognized Credential
  • Career Advancement Credential
  • Shareable Achievement
certificate

Frequently Asked Questions

Looking for answers to other questions?

Agentic AI refers to systems where AI can plan, decide, and act across multiple steps using tools and memory. It is important because modern AI applications increasingly require autonomy, reasoning, and workflow-driven problem solving.

You will build newsletter automation agents, deep research agents, data analyst agents, database-aware agents, and multi-agent resume review systems, covering diverse use cases across content, data, research, and HR domains.

The projects are designed around realistic business scenarios such as research automation, analytics workflows, database querying, content generation, and resume screening, making them highly relevant to industry and enterprise AI applications.

A Data Analyst AI Agent analyzes datasets, runs queries, generates insights, and explains results in natural language, combining data tools with reasoning to support decision-making and analytics workflows.

A Database AI Agent interacts directly with structured databases, understands schemas, generates queries, retrieves results, and reasons over data, enabling natural-language access to databases using AI agents.

A Deep Research AI Agent performs multi-step research by gathering data from multiple sources, synthesizing insights, validating information, and producing structured research outputs using agentic reasoning and planning.

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