Agentic AI Applications with Claude Code

About This Course

Agentic AI Applications with Claude Code equips learners with practical skills to build intelligent, context-aware applications using Claude Code, Retrieval-Augmented Generation (RAG), reusable skills, and autonomous AI agents. The course emphasizes context engineering and RAG-based memory, enabling AI applications to retrieve relevant knowledge, maintain project context, and deliver more accurate and consistent outputs across complex workflows.

Learners will explore Claude Code fundamentals, including project initialization, operating modes, vibe coding workflows, and memory management. They will learn to structure persistent memory using project instructions, documentation, knowledge files, and external data sources so that Claude Code can understand application requirements, development standards, and domain-specific knowledge.

The course covers tools, custom slash commands, and Model Context Protocol (MCP) integrations for connecting Claude Code with external systems, APIs, databases, and development tools. Participants will also learn to create reusable skills that combine instructions, knowledge, tools, and workflows for specialized tasks.

Through hands-on activities, learners will implement RAG pipelines, develop skills and sub-agents, and use hooks to automate and control agentic workflows. They will learn how agents can retrieve knowledge, invoke tools, coordinate tasks, and execute multi-step processes with greater autonomy and reliability.

The course also addresses testing, debugging, optimization, and issue reporting for RAG and AI agent applications. By the end of the course, learners will be able to implement foundation-model applications, deploy agentic AI workflows, and identify issues affecting retrieval quality, memory accuracy, tool execution, and agent performance.