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Avis du cours The Agentic AI Engineering Masterclass 2026 par Prof. Ryan Ahmed

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

Format d'apprentissage Online Course

Sous-catégorie AI Agents

Prix du cours $19.99 (list $10.99)

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À propos de Prof. Ryan Ahmed

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

Qui est Prof. Ryan Ahmed ?

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12,696 students

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Langue du cours

English

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Que apprend-on dans le cours de Prof. Ryan Ahmed ?

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.

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