Mastering Agentic AI: From Prompt to Protocols to Production

About this course

Dive into the Agentic AI Revolution and transform from prompt engineer to production architect, building autonomous systems that perceive, reason, and orchestrate complex workflows at scale. In Mastering Agentic AI: From Prompt to MCP-A2A to Production (37+ hours), you'll master LLM API integrations—provider-agnostic with DeepSeek examples for cost optimization—to architect intelligent agents leveraging MCP (Model Context Protocol) for universal tool interoperability and A2A (Agent-to-Agent) for distributed coordination in the 2025 ecosystem.

Whether you're an AI engineer debugging multi-step reasoning chains, a backend developer scaling ML infrastructure, or a research scientist pushing boundaries in autonomous systems, this course delivers battle-tested, production-grade expertise. Starting with threat modeling and least-privilege security from Day 1, you'll navigate the agentic spectrum: from perception modules and LLM reasoning engines to action-reflection loops that suppress hallucinations and enforce safe tool execution.

Master advanced prompting as code: implement Chain-of-Thought (CoT) for step-by-step reasoning, Self-Consistency for multi-path validation, Tree of Thoughts (ToT) for parallel exploration, and the ReAct framework (Reasoning + Acting) for tool-augmented problem-solving. Optimize via flexible LLM API calls, A/B testing, and versioned prompt management with automated eval suites.

Build hierarchical memory architectures: deploy Retrieval-Augmented Generation (RAG) pipelines with vector embeddings, hybrid semantic-keyword search, rerankers for precision, and episodic memory with decay/summar

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