Design, build, and deploy production-ready multi-agent AI systems with tools, memory, and real workflows
Sub Category
- Data Science
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Objectives
- Understand how Agentic AI systems work (agents, reasoning, tools, memory)
- Build single-agent systems with tool usage and retrieval-based memory
- Design and implement multi-agent architectures for complex workflows
- Use frameworks like LangGraph, CrewAI, and AutoGen for orchestration
- Integrate RAG (Retrieval-Augmented Generation) for grounded, accurate responses
- Connect agents to real-world systems via APIs and automation tools (Zapier, n8n)
- Implement evaluation pipelines (LLM-as-a-Judge, test cases, regression testing)
- Add guardrails and safety mechanisms to prevent failures and misuse
- Monitor systems with logging, observability, and performance tracking
- Design scalable, production-ready AI architectures for enterprise use cases
Pre Requisites
- Basic understanding of AI concepts (helpful but not required)
- Familiarity with Python is useful, but not mandatory
- Comfortable using web apps and APIs (beginner level is enough)
- Laptop with internet connection (Mac/Windows/Linux)
- Ability to install tools like: Python and VS Code (or any IDE)
- Willingness to learn hands-on by building real systems
- No prior experience with agents or LLM frameworks required
- Curiosity about AI, automation, and real-world applications
FAQ
- Q. How long do I have access to the course materials?
- A. You can view and review the lecture materials indefinitely, like an on-demand channel.
- Q. Can I take my courses with me wherever I go?
- A. Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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Coupon Code(s)