Build Agentic AI with MCP-A2A Protocols: Advanced Prompting, RAG Memory, Tool Calling & Multi-Agent System to Production
Sub Category
- Other IT & Software
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Objectives
- Master the core components of agentic AI, including perception, reasoning, action loops, and LLM limitations for building autonomous systems.
- Apply advanced prompting techniques like Chain-of-Thought, ReAct, and Tree of Thoughts to enhance LLM reasoning and solve complex multi-step tasks.
- Design RAG pipelines, vector databases, and episodic memory systems to enable agents with long-term knowledge retention and autonomous retrieval.
- Build, test, and deploy scalable multi-agent systems using MCP/A2A protocols, with robust safety guardrails, evaluation metrics, and production optimizations.
Pre Requisites
- Key Prerequisite: Basic proficiency in Python programming and familiarity with large language models (LLMs) or APIs.
- Accessibility Note: No prior agent-building experience required—ideal for motivated beginners with foundational AI knowledge; we'll ramp up on tools like Prometheus, Jaeger, ELK Stack, and DeepSeek API usage through practical examples.
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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