The Agentic AI Engineering Masterclass (2025 Edition)

Build AI Agents 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 masterclass, 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.

Who this course is for:
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.