{"id":14896,"date":"2026-08-10T11:20:32","date_gmt":"2026-08-10T11:20:32","guid":{"rendered":"https:\/\/nokobox.com\/index.php\/item\/udemy-build-ai-employees-with-django-agentic-ai-for-developers-2026-6\/"},"modified":"2026-08-10T11:20:32","modified_gmt":"2026-08-10T11:20:32","slug":"udemy-build-ai-employees-with-django-agentic-ai-for-developers-2026-6","status":"publish","type":"digital_item","link":"https:\/\/nokobox.com\/index.php\/item\/udemy-build-ai-employees-with-django-agentic-ai-for-developers-2026-6\/","title":{"rendered":"Udemy \u2013 Build AI Employees with Django \u2013 Agentic AI for Developers 2026-6"},"content":{"rendered":"
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Descriptions<\/h2>\n

Build AI Employees with Django \u2013 Agentic AI for Developers, Are you a Python or Django developer who wants to stay ahead in the AI era? This hands-on, project-based course teaches you how to build real AI Employees, which are autonomous AI agents that work for your business 24\/7, handle customer queries, collaborate with each other, assess fraud risk, and make decisions without human intervention. You will build a complete AI-powered customer support system for a fictional AC company called CoolBreeze AC. By the end of this course, you will have built Maya, an AI support agent that handles customer queries and decides when to escalate; a manager agent that reviews escalated cases and makes refund decisions; a risk agent that assesses fraud patterns; a RAG system that gives agents access to real company documents to prevent hallucinations; a live streaming support dashboard where staff can monitor every agent decision in real time; and a full deployment on Railway to make your system live on a real URL.<\/p>\n

Throughout this journey, you will learn how Agentic AI actually works under the hood, including the agent loop, tool calling, and decision making. You will discover how to turn Django ORM queries into AI agent tools, write effective tool schemas that guide an LLM\u2019s decision making, and build multi-agent systems where agents collaborate and hand off tasks autonomously. The course also covers implementing RAG using ChromaDB and pypdf so agents answer from real company documents, understanding embeddings and vector similarity search, and streaming live agent activity to a dashboard using Server Sent Events. Unlike most AI courses that jump straight into frameworks, we build everything from scratch first to ensure you understand the foundational concepts before exploring how LangChain abstracts them. This course is designed for Django and backend developers who want to add Agentic AI to their skill set and build real, resume-worthy AI projects from scratch.<\/p>\n

What you\u2019ll learn<\/h3>\n