Mastering
Agentic AI
Build autonomous AI systems, multi-agent pipelines, and production-grade
LLM applications with LangChain, LangGraph & beyond.
What you’ll build
From prompts to
autonomous agents
Go beyond basic chatbots. Learn to design LLM-powered agents that plan, use tools, retain memory, and collaborate in multi-agent swarms — all with clean, production-ready Python.
Every module combines theory with hands-on projects: a research agent, a code-execution sandbox, a RAG pipeline, and a fully orchestrated multi-agent system.
Curriculum
Course Modules
Understand what makes an LLM “agentic” — reasoning loops, tool use, and the ReAct framework.
FoundationsDeep-dive into LCEL, prompt templates, output parsers, and building composable chains.
LangChainGive agents superpowers: web search, code execution, APIs, databases, and custom tool creation.
ToolingImplement conversation buffers, entity memory, vector-store memory, and episodic recall.
MemoryBuild end-to-end RAG systems with chunking strategies, vector stores, and hybrid retrieval.
RAGOrchestrate complex agent workflows as graphs with conditional edges, loops, and human-in-the-loop.
LangGraphTechnologies
The complete stack
Hands-on code
Build a ReAct Agent
in 20 lines
# Build a fully autonomous ReAct agent with LangGraph from langchain_openai import ChatOpenAI from langchain_community.tools.tavily_search import TavilySearchResults from langgraph.prebuilt import create_react_agent # 1. Define the model model = ChatOpenAI(model="gpt-4o", temperature=0) # 2. Give the agent tools tools = [TavilySearchResults(max_results=3)] # 3. Compile the graph — that's it! agent = create_react_agent(model, tools) # 4. Run autonomously result = agent.invoke({ "messages": [("human", "What are the latest breakthroughs in AI agents?")] }) print(result["messages"][-1].content)
Ready to build the
future of AI?
Join thousands of engineers mastering autonomous systems. Start your journey from zero to production-grade agentic AI.
