Become An Ai Agent & Workflow Automation Engineer In 2025

Build Agentic AI Workflows Using OpenAI Agents SDK, LangGraph, N8N, CrewAI, AutoGen, CoPilot, ChatGPT Agents, & MCP!

What you'll learn

Build and deploy intelligent autonomous AI agents using cutting-edge frameworks like OpenAI Agents SDK, N8N, AutoGen, CrewAI, LangGraph, & MCP.

Build AI agents that remember, reason, and collaborate using memory, tools, guardrails, and handoffs.

Learn the foundational components of the OpenAI Agents SDK, including the Agent object and Runner class.

Build and run AI agents and monitor their activity using traces on the OpenAI API platform.

Build handoff mechanisms that smoothly transfer context and inputs between agents (e.g., Planner → Writer).

Implement guardrails to enforce boundaries (e.g., preventing responses on restricted topics like politics).

Explore CrewAI for building more advanced agentic workflows and extend agents with custom Python execution tools for analysis and modeling.

Grasp the fundamentals of multi-model AI agents in AutoGen and build teams of agents using different LLMs (e.g., GPT, Gemini, Claude).

Understand how to design agentic workflows in LangGraph, including connecting them to interfaces like Gradio for user interaction.

Use n8n for low-code automation, building AI-powered flows that integrate with Google Sheets, Calendar, and Gmail.

Learn the principles of the Model Context Protocol (MCP) for tool interoperability and build agents that interact with MCP services.

Build manager functions to orchestrate multi-agent workflows from input to final deliverable.

Build AI agents that integrate Tavily web search for structured, real-time search results.

Extend agents by integrating OpenAI tools (e.g., Code Interpreter) and combining real-time search, memory, and reasoning into workflows.

Apply memory-enabled agents to real use cases (e.g., market research assistant) for multi-turn queries.

Develop a library of specialist agents (Planner, Writer, Analyst, Search Agent) and coordinate their interactions.

Create collaborative agent teams for real-world tasks like marketing strategy, with the option of adding a human-in-the-loop User Proxy for oversight.

Build domain-specific LangGraph agents (e.g., flights and hotel booking) and define custom tools for task-specific workflows.

Create tools as agents by wrapping autonomous agents behind a function-tool interface, enabling seamless invocation by others.

Design a multi-agent research assistant that can triage queries, delegate tasks, and generate executive-ready reports.

Design creative multi-agent pipelines for advertising campaigns, with role-specific agents like Creative Director, Strategist, and Copywriter.

Create and deploy Gradio-based MCP tools as standardized services accessible to agents.

Create collaborative agent teams for real-world tasks like marketing strategy, with the option of adding a human-in-the-loop User Proxy for oversight.

Requirements

You will need a laptop and an internet connection!

No programming experience required; basic Python skills are a plus.

Description

In this hands-on course, you’ll learn how to design, build, and deploy next-generation AI agents that combine memory, tools, collaboration, and automation to solve real-world problems. Starting with the OpenAI Agents SDK, you’ll explore how to create simple agents and gradually extend them with advanced features such as persistent memory, guardrails, and smooth handoffs between workflows.You’ll then dive into multi-agent systems, where specialized agents, like researchers, analysts, and writers, work together, passing context and outputs to build complex deliverables. Along the way, you’ll learn how to orchestrate these systems with manager functions, enforce ethical and domain boundaries with guardrails, and design creative pipelines for use cases from market research to advertising campaigns.The course introduces multiple frameworks for building production-ready agentic workflows. You’ll explore AutoGen for multi-model collaboration, LangGraph for modular pipelines connected to user interfaces, and CrewAI for advanced orchestration. You’ll also learn how to extend agents with custom tools, from Python code execution for data analysis to classical machine learning models like linear regression, random forest, and XGBoost.You’ll gain practical experience with the Model Context Protocol (MCP), enabling agents to interoperate with standardized external services, and learn how to build and deploy MCP tools using Gradio. Finally, you’ll see how low-code platforms like n8n can bring everything together into seamless automation flows, integrating Gmail, Google Sheets, Google Calendar, and AI models to create complete end-to-end systems.By the end of the course, you’ll have the skills to:Build AI agents with memory, tools, and reasoning capabilities.Orchestrate multi-agent workflows for research, analysis, and creative tasks.Integrate guardrails, handoffs, and oversight to ensure safe, reliable outputs.Deploy advanced agentic workflows across AutoGen, LangGraph, CrewAI, and MCP.Automate business processes with low-code tools like n8n connected to real-world apps.Whether you’re a developer, data scientist, or business innovator, this course equips you with the full toolkit to design AI systems that collaborate, automate, and scale in production.

Overview

Section 1: Introduction and Welcome to the Course!

Lecture 1 Welcome to the Course & AI Agents Demo!

Lecture 2 Download the Course Materials

Lecture 3 Course Outline & Key Learning Objectives

Lecture 4 Key Success Tips

Lecture 5 AI Agents in Production

Lecture 6 Environment Setup & Anaconda Download for Mac, Windows, & Linux

Section 2: Open AI Agents SDK Framework (Single-Agent)

Lecture 7 Project 1: Build Simple AI Agents with No Memory & No Tools

Lecture 8 Task 1. Introduction & Module Objectives - Build Simple AI Agents

Lecture 9 Task 2. Environment Setup and OpenAI API Configuration

Lecture 10 Task 3. Build and Run Our First AI Agent (No Memory & No Tools)

Lecture 11 Practice Opportunity Question: Change Model & Test Agent with New Input

Lecture 12 Practice Opportunity Solution: Change Model & Test Agent with New Input

Lecture 13 Task 4. Memory Test, Tokenization, & OpenAI API Traces

Lecture 14 Practice Opportunity Question: Build Tweet Generator AI Agent

Lecture 15 Practice Opportunity Solution: Build Tweet Generator AI Agent

Lecture 16 Project 2: Build an AI Agent with Memory

Lecture 17 Task 1. Project Overview and Key Learning Objectives - AI Agents with Memory

Lecture 18 Task 2. Build a stateless AI Agent with No Memory

Lecture 19 Task 3. Build an AI Agent with Memory

Lecture 20 Practice Opportunity Question: Build A Travel Planner AI Agent + Memory

Lecture 21 Practice Opportunity Solution: Build A Travel Planner AI Agent + Memory

Lecture 22 Project 3: Build AI Agents with Tools

Lecture 23 Task 1. Project Overview and Key Learning Objectives - AI Agents with Tools

Lecture 24 Task 2. Setup Tavily Search API

Lecture 25 Task 3. Create a Tavily Search Function and Develop a Tool

Lecture 26 Practice Opportunity Question: Tavily Search Function & Tool

Lecture 27 Practice Opportunity Solution: Tavily Search Function & Tool

Lecture 28 Task 4. Build and Run AI Agents with Tavily Search Tool

Lecture 29 Practice Opportunity Question: Test AI Agents With Tools & Memory

Lecture 30 Practice Opportunity Solution: Test AI Agents With Tools & Memory

Lecture 31 Task 5. Leverage Existing OpenAI Built-In Tools

Section 3: Open AI Agents SDK Framework (Multi-Agent)

Lecture 32 Task 1. Project Overview & Key Learning Objectives - Building Multi Agent Teams

Lecture 33 Task 2. Setup OpenAI API & Required Tools

Lecture 34 Task 3. Define Two AI Agents in OpenAI Agents SDK (Researcher & Analyst Agents)

Lecture 35 Practice Opportunity Question: Run Both AI Agents

Lecture 36 Practice Opportunity Solution: Run Both AI Agents

Lecture 37 Task 4. Define a Writer Agent for Automatic Report Generation

Lecture 38 Practice Opportunity Question: Update the Writer Agent Instructions

Lecture 39 Practice Opportunity Solution: Update the Writer Agent Instructions

Lecture 40 Task 5. Build a Manager for Multiple AI Agents Orchestration + Trace Execution

Lecture 41 Practice Opportunity Question - Develop a Creative Advertising AI Agents Team

Lecture 42 Practice Opportunity Solution - Develop a Creative Advertising AI Agents Team

Lecture 43 Task 1. Project Overview & Key Learning Objectives - Guardrails & Handoffs

Lecture 44 Task 2. Setup OpenAI API & Tools

Lecture 45 Build AI Agents with Guardrails

Lecture 46 Practice Opportunity Question: AI Agents with Guardrails

Lecture 47 Practice Opportunity Solution: AI Agents with Guardrails

Lecture 48 Task 4. Define a Team of AI Agents (Fundamentals & Analyst AI Agents)

Lecture 49 Task 5. Create AI Agents As Tools

Lecture 50 Practice Opportunity Question: Multi-Agent Traces on OpenAI API Platform

Lecture 51 Practice Opportunity Solution: Multi-Agent Traces on OpenAI API Platform

Lecture 52 Task 6. AI Agents with Handoffs

Lecture 53 Practice Opportunity Question: AI Agents as Tool

Lecture 54 Practice Opportunity Solution - AI Agents as Tool

Section 4: AutoGen FrameWork

Lecture 55 Task 1: Introduction & Goals – Build AI Agent Teams with AutoGen

Lecture 56 Task 2: Explore AutoGen Capabilities & Key Features

Lecture 57 Practice Opportunity Question: AI Agents Teams Design

Lecture 58 Practice Opportunity Solution: AI Agents Teams Design

Lecture 59 Task 3: Your First Build – Creating AI Agents in AutoGen (GPT-4o)

Lecture 60 Practice Opportunity Question: Building AI Agents in AutoGen

Lecture 61 Practice Opportunity Solution: Building AI Agents in AutoGen

Lecture 62 Task 4. Test AI Agents Conversations with Similar LLM (OpenAI GPT-4o)

Lecture 63 Practice Opportunity Question: Modify initiate_chat() Function Parameters

Lecture 64 Practice Opportunity Solution: Modify initiate_chat() Function Parameters

Lecture 65 Task 5. Configure Multi-Model AI Agents in AutoGen with Gemini & OpenAI's GPT-4o

Lecture 66 Practice Opportunity Question: Configure AI Agents using Anthropic's Claude

Lecture 67 Practice Opportunity Solution: Configure AI Agents using Anthropic's Claude

Lecture 68 Task 6. Trigger Multi-Model AI Agents Conversations in AutoGen

Lecture 69 Practice Opportunity Question: Adjusting AI Agent's Creativity Level

Lecture 70 Practice Opportunity Solution: Adjusting Agent's Creativity Level

Lecture 71 Task 7. Adding Human (User Proxy Agent) & Leveraging Group Chat

Lecture 72 Practice Opportunity Question: Adding Claude's Social Media Strategist to Chat

Lecture 73 Practice Opportunity Solution: Adding Claude's Social Media Strategist to Chat

Lecture 74 Conclusion, Summary, & Thank You Message!

Section 5: LangGraph FrameWork

Lecture 75 Task 1. Project Kickoff: Crafting AI Agentic Workflows in LangGraph

Lecture 76 Task 2. Master LangGraph Components: Nodes, Edges & State Graph Essentials

Lecture 77 Task 3. Build Your First AI Agentic Workflow – Part 1

Lecture 78 Task 3. Build Your First AI Agentic Workflow – Part 2

Lecture 79 Practice Opportunity Question: Test Summarization AI Agent in LangGraph

Lecture 80 Practice Opportunity Solution: Test Summarization AI Agent in LangGraph

Lecture 81 Task 4. Create a Multi-Node AI Agentic Workflow in LangGraph

Lecture 82 Practice Opportunity Question: Add a Sentiment Node to an AI Agentic Workflow

Lecture 83 Practice Opportunity Solution: Add a Sentiment Node to an AI Agentic Workflow

Lecture 84 Task 5. Build an AI Workflow with One Tool & Conditional Edges – Part 1

Lecture 85 Task 5. Build an AI Workflow with One Tool & Conditional Edges – Part 2

Lecture 86 Practice Opportunity Question: Calling Tools in LangGraph

Lecture 87 Practice Opportunity Solution: Calling Tools in LangGraph

Lecture 88 Task 6. Create & Integrate a Custom Tool in LangGraph Workflows

Lecture 89 Practice Opportunity Question: Define New Custom Tools in LangGraph

Lecture 90 Practice Opportunity Solution: Define New Custom Tools in LangGraph

Lecture 91 Task 7. Use LangGraph + Amadeus to Build a Flight Search Tool with ToolNode

Lecture 92 Practice Opportunity Question: Adding Hotel Search Tool Using Amadeus

Lecture 93 Practice Opportunity Solution: Adding Hotel Search Tool Using Amadeus

Lecture 94 Task 8. Combine All Features to Build the AI Booking Agent

Lecture 95 Practice Opportunity Question: Test the AI Agent Booking Tool

Lecture 96 Practice Opportunity Solution: Test the AI Agent Booking Tool

Lecture 97 Task 9. Integrate the AI Booking Agent with Gradio in LangGraph

Lecture 98 Summary, Wrap-Up, & Thank You Message!

Section 6: CrewAI FrameWork

Lecture 99 Task 1. Project Kickoff: Building a Data Science AI Team with CrewAI

Lecture 100 Task 2. Regression Models: Training & Evaluation Overview

Lecture 101 Task A. Hands-On Project Intro: ML Regression

Lecture 102 Task B. Regression 101: Foundations of Machine Learning

Lecture 103 Practice Challenge Question: Test Your Regression Basics

Lecture 104 Practice Challenge Solution: Test Your Regression Basics

Lecture 105 Task C. Data Inspection Part 1: Importing Libraries & First Look

Lecture 106 Task C. Data Inspection Part 2: Importing Libraries & First Look

Lecture 107 Practice Opportunity Question: Inspecting Data in Python

Lecture 108 Practice Opportunity Solution: Inspecting Data in Python

Lecture 109 Task D. Managing Missing Data: Imputation Techniques

Lecture 110 Practice Opportunity Question: Data Imputation & Handling Missing Dataset

Lecture 111 Practice Opportunity Solution: Data Imputation & Handling Missing Dataset

Lecture 112 Task E. Data Visualization & Exploration

Lecture 113 Practice Opportunity Question: Visualization & Exploration

Lecture 114 Practice Opportunity Solution: Visualization & Exploration

Lecture 115 Task F. Data Pre-Processing & Splitting (Training Vs. Testing)

Lecture 116 Practice Opportunity Question: Data Pre-Processing & Splitting

Lecture 117 Practice Opportunity Solution: Data Pre-Processing & Splitting

Lecture 118 Task G. Scikit-Learn for ML Regression

Lecture 119 Practice Opportunity Question: Scikit-Learn for ML Regression

Lecture 120 Practice Opportunity Solution: Scikit-Learn for ML Regression

Lecture 121 Task H. Scikit-Learn for Random Forest Regression

Lecture 122 Practice Opportunity Question: XG-Boost Regression

Lecture 123 Practice Opportunity Solution: XG-Boost Regression

Lecture 124 Task I. Feature Importance Analysis

Lecture 125 Practice Opportunity Question: Feature Importance Analysis

Lecture 126 Practice Opportunity Solution: Feature Importance Analysis

Lecture 127 Task 3. Explore CrewAI Core Elements (Agents, Tasks, Tools)

Lecture 128 Task 4. Load & Validate the NotebookCodeExecutor Tool

Lecture 129 Practice Opportunity Question: Run the NotebookCodeExecutor Tool

Lecture 130 Practice Opportunity Solution: Running the NotebookCodeExecutor Tool

Lecture 131 Task 5. Set Up Multiple AI Agents in CrewAI

Lecture 132 Practice Opportunity Question: Adjust Existing AI Agents

Lecture 133 Practice Opportunity Solution: Adjusting Existing AI Agents

Lecture 134 Task 6. Map Out Key Tasks in CrewAI & Responsible Agents

Lecture 135 Task 7. Build & Assemble the Crew + Automate a Data Science Workflow

Lecture 136 Practice Opportunity Question: Modify Tasks to Create Decision Trees

Lecture 137 Practice Opportunity Solution: Modifying Tasks for Decision Tree Creation

Lecture 138 Summary & Closing Insights

Section 7: Model Context Protocol (MCP)

Lecture 139 Task 1. Project Overview & Introduction to Model Context Protocol (MCP)

Lecture 140 Task 2. Deep Dive into Model Context Protocol (MCP)

Lecture 141 Task 3. Setting Up Libraries & API Configuration

Lecture 142 Task 4A (Part 1). Building the MCP Server with Tools

Lecture 143 Task 4A (Part 2). Continuing MCP Server Build & Tool Integration

Lecture 144 Task 4B. Launching the MCP Server

Lecture 145 Practice Opportunity Question: Adding a New Tool to the MCP Server

Lecture 146 Practice Opportunity Solution: Adding a New Tool to the MCP Server

Lecture 147 Task 5. Explore Tools on MCP Server and Fetch the Manifest (Schema)

Lecture 148 Practice Opportunity Question: MCP Server Manifest (Schema)

Lecture 149 Practice Opportunity Solution: MCP Server Manifest (Schema)

Lecture 150 Task 6. Create an AI Agent Using OpenAI Agents SDK With MCP Tools

Lecture 151 Conclusion, Summary, & Thank You!

Section 8: N8N (No-Code) FrameWork

Lecture 152 Introduction to n8n: Features, Workflow Basics, and Learning Goals

Lecture 153 Build Your First Agentic AI Summarization Workflow in n8n

Lecture 154 Export Workflows, Manage Variables, and Track Logs

Lecture 155 Practice Opportunity Question: Create Translation Agentic Workflow with Claude

Lecture 156 Practice Opportunity Solution: Create Translation Agentic Workflow with Claude

Lecture 157 Adding Search, Memory, and Exploring n8n Templates

Lecture 158 Practice Opportunity Question: Test Agent Search Capabilities

Lecture 159 Practice Opportunity Solution: Test Agent Search Capabilities

Lecture 160 Integrating Google Sheets into Agentic Workflows with n8n

Lecture 161 Practice Opportunity Question: Build a Python Conversion Agentic Workflow

Lecture 162 Practice Opportunity Solution: Build a Python Conversion Agentic Workflow

Lecture 163 Generate Structured Output with the Output Parser in n8n

Lecture 164 Automate Calendar Scheduling with Google Calendar Workflows

Lecture 165 Adding Email Triggering Capabilities

Section 9: Congratulations on Completing the Course!

Lecture 166 Congratulations!

Data scientists, ML engineers, and AI researchers who want to build AI Agents.,Software developers with basic Python skills who want to integrate cutting-edge LLMs and agent frameworks into real-world applications.,Entrepreneurs and startup Founders wanting to build AI-powered autonomous agents.,Corporate innovation teams or R&D teams wanting to prototype AI-powered workflows, assistants, and automations.,Advanced students and educators looking for practical, hands-on experience with Agentic AI Engineering.