Practical Agentic AI: RAG, Planning & Vector Search

About this course

This course contains the use of artificial intelligence.

Generative AI is moving from reactive “knowers” to proactive “doers” that perceive, plan, and act toward goals. This shift—Agentic AI—pairs LLM reasoning with tools, memory, and workflows so systems can execute multi-step tasks autonomously. Enterprises now expect agents that ground answers with RAG, orchestrate APIs, and operate reliably with guardrails—raising new questions about autonomy, accountability, and oversight.

What This Course Covers

You’ll learn an end-to-end Agentic AI stack: the Perceive→Reason→Act loop; Retrieval-Augmented Generation; planning & memory; the MCP (Model–Controller–Prompter) workflow; and framework choices (LangChain, LlamaIndex, CrewAI, AutoGen). We translate concepts into an applied build: a CLI “Personalized News Curator” that uses Tavily for live search, ChatGPT/Gemini for ranking & summaries, an in-memory/SQLite → ChromaDB store, topic-pillar weighting, semantic re-ranking, and explanation generation.

What You Will LearnDifferentiate LLMs vs. Agentic AI across autonomy, memory, and tool use.

Apply the Perceive→Reason→Act loop to real tasks.

Implement MCP (Model–Controller–Prompter) orchestration for agents.

Ground responses with RAG for factuality and reliability.

Build a CLI agent that collects preferences and runs a continuous recommendation loop.

Integrate Tavily search + ChatGPT/Gemini for retrieval and ranking.

Persist interaction history (SQLite) and migrate to ChromaDB embeddings.

Engineer topic “pillars,” weighted selection, and semantic re-ranking.

Generate user-facing explanations for recommendations (XAI).

Address agent risks: memory poisoning, goal manipulation, identity spoofing.

Compare frameworks (LangChain, LlamaIndex, CrewAI, AutoGen) to match goals.

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