Agentic AI Development — Lecture Series

A hands-on lecture series building from LLM fundamentals to production multi-agent systems.

Lecture Index

#	Title	Topics
Lecture 01	LLM Fundamentals for Agents	Transformers, tokenization, inference mechanics, context windows
Lecture 02	Prompt Engineering & Structured Output	System prompts, few-shot, JSON mode, function calling
Lecture 03	Tool Use & Function Calling	Tool schemas, parallel calls, error handling, safety
Lecture 04	Agent Architecture Patterns	ReAct, CoT, Reflexion, plan-and-execute
Lecture 05	Memory Systems	Short-term, long-term, episodic, semantic memory
Lecture 06	LangGraph — Stateful Workflows	Nodes, edges, state, checkpointing, human-in-the-loop
Lecture 07	Claude Agent SDK	Subagents, tool loops, streaming, computer use
Lecture 08	Multi-Agent Systems	CrewAI, AutoGen, supervisor patterns, coordination
Lecture 09	RAG — Ingestion & Embeddings	Chunking, embedding models, vector stores, indexing
Lecture 10	RAG — Retrieval & Reranking	Hybrid search, MMR, cross-encoder reranking, evaluation
Lecture 11	Evaluation & Observability	LLM-as-judge, RAGAS, tracing, cost tracking
Lecture 12	Production Deployment	Streaming, caching, model routing, safety, scaling

Lab Index

#	Title	Build
Lab 01	Research Agent with Tool Use	Web search + code execution + citations
Lab 02	Multi-Agent Code Review	Planner → Coder → Reviewer → Summarizer
Lab 03	Production RAG System	Ingestion pipeline + hybrid search + RAGAS eval

Prerequisites

• Python 3.10+
• PyTorch basics (Phase 3 Core — Neural Networks)
• API keys: Anthropic, OpenAI (optional)

pip install anthropic langchain langgraph langchain-anthropic \
            chromadb sentence-transformers ragas openai