Learn how to build production-ready Agentic AI systems that can reason, retrieve information, use tools, make decisions, and execute multi-step tasks autonomously. This hands-on course covers the core technologies and engineering practices required to move beyond basic LLM applications and build reliable AI agents.

You will explore modern agentic architectures, retrieval techniques, orchestration frameworks, safety mechanisms, and evaluation strategies through practical, real-world use cases.

Key Points Covered

RAG (Retrieval-Augmented Generation): Build AI systems that retrieve relevant information from external knowledge sources to generate accurate, context-aware responses.
LangChain: Learn how to develop LLM-powered applications, integrate tools and models, and create reusable AI workflows.
LangGraph: Build stateful, multi-step, and cyclic agent workflows with greater control over agent execution and decision-making.
Guardrails: Implement safety, validation, access controls, and output constraints to make AI agents more reliable and responsible.
Evals: Learn how to evaluate agent performance, measure response quality, identify failure cases, and continuously improve AI systems.
Vectorless RAG: Explore retrieval approaches that reduce or eliminate dependence on vector databases by leveraging alternative search and retrieval techniques.

What You Will Gain

By the end of the course, you will understand how to design, build, orchestrate, secure, and evaluate agentic AI applications capable of solving complex, knowledge-intensive tasks.