Udemy – The Agentic AI Engineering Masterclass 2025 2025-9

The Agentic AI Engineering Masterclass 2025. This is a comprehensive, hands-on training program that teaches participants how to design, develop, and deploy the next generation of AI agents. Focusing on solving real-world problems by combining memory, tools, interoperability, and automation, the course begins with the OpenAI Agent SDK and progresses step-by-step from creating simple agents to implementing advanced features such as persistent memory, guardrails, and workflow integration. It then moves on to multi-agent systems, where expert agents such as researchers, analysts, and writers collaborate and share context and outputs to produce complex products. Participants learn how to align these systems with management functions, apply ethical and boundary-setting frameworks with guardrails, and design creative pipelines for use cases ranging from market research to advertising campaigns. This course introduces several production-ready frameworks for building agent workflows, including AutoGen for multi-modal collaboration, LangGraph for modular pipelines connected to user interfaces, and CrewAI for advanced coordination. Participants will also learn how to extend the capabilities of agents with custom tools, from running Python code for data analysis to integrating classic machine learning models such as linear regression, random forests, and XGBoost.

What you will learn

• Build and deploy AI agents: Build intelligent, autonomous AI agents using advanced frameworks such as OpenAI Agents SDK, N8N, AutoGen, CrewAI, LangGraph, and MCP.
• Creating Agents with Memory, Logic, and Tools: Learn how to build AI agents that remember, reason, and cooperate using memory, tools, guardrails, and transitions.
• Learn the basics of the OpenAI Agents SDK: Become familiar with the fundamental components of the OpenAI Agents SDK, including the Agent object and the Runner class.
• Monitoring Agent Activity: Learn how to build, run agents, and monitor their activity using traces in the OpenAI API platform.
• Building transfer mechanisms: Build transfer mechanisms to smoothly transfer context and inputs between agents (Planner to Writer).
• Implement guardrails: Implement guardrails to enforce boundaries (such as preventing responses on restricted topics).
• Explore advanced frameworks: Explore CrewAI to build advanced agent workflows and extend agents with custom Python tools for analysis and modeling.
• Building Agent Teams: Learn the principles of multi-model AI agents in AutoGen and build teams of agents with different LLMs (GPT, Gemini, Claude).
• Designing Agent Workflows in LangGraph: Understand how to design agent workflows in LangGraph, including connecting them to interfaces like Gradio for user interaction.
• Automation with low-code tools: They use n8n for low-code automation and build AI-powered workflows that integrate with Google Sheets, Calendar, and Gmail.
• Understand MCP Protocol Principles: Learn the principles of the Model Context Protocol (MCP) for tool interoperability and build agents that interact with MCP services.
• Build management functions: Build management functions to coordinate multi-agent workflows from input to final product.
• Integration with search tools: Build AI agents that integrate Tavily web search for structured, real-time search results.
• Expanding OpenAI tools: Extending agents by integrating OpenAI tools (such as Code Interpreter) and incorporating real-time search, memory, and reasoning into workflows.
• Build agent teams for real tasks: Create teams of collaborative agents for real tasks like marketing strategy, with the option to add a human user proxy for monitoring.

This course is suitable for people who:

• Data scientists, machine learning engineers, and AI researchers who want to build AI agents.
• Software developers with basic Python skills who want to integrate advanced LLMs and agent frameworks into real-world applications.
• Entrepreneurs and startup founders looking to build AI-powered autonomous agents.
• Enterprise innovation teams or R&D teams that want to prototype AI-based workflows, assistants, and automations.
• Advanced students and educators seeking practical, hands-on experience with agent-based artificial intelligence engineering.