Full Stack Agentic AI Engineering Course
What You’ll Learn
Building AI Agents is no longer just theory — this specialized course teaches you to build production-ready Agentic AI systems. With LangGraph, GraphRAG (Neo4j), MCP Tool Contracts, and no-code n8n workflows, you will be able to deploy enterprise-grade AI pipelines.
Benefits
- LangGraph — State, Reducers, Branching, Checkpointing
- GraphRAG — Knowledge Graphs with Neo4j
- Multi-Agent Orchestration (CrewAI, AutoGen)
- MCP Tool Contracts & Protocols
- Agentic RAG Pipelines
- n8n No-Code AI Workflows
- Production Deployment on AWS\
Curriculum
⚡ Module 1 — LangGraph Deep Dive
- LangGraph Architecture: StateGraph, Nodes, Edges, Reducers
- Conditional Branching, Parallel Execution, Agent Loops
- Memory: In-memory, Persistent Checkpointing (SQLite, Redis)
- Human-in-the-Loop: Interrupt, Review, Approve workflows
- Subgraphs, Streaming, Error Recovery & Retry Logic
- Project: LangGraph-based Research Agent with memory
🏗️ Module 2 — Advanced RAG & GraphRAG
- Standard RAG Pipeline review: Chunking, Embedding, Retrieval, Generation
- Advanced Retrieval: Hybrid Search, Re-ranking, HyDE, Contextual Compression
- Agentic RAG: Self-querying, Corrective RAG, Adaptive Retrieval
- GraphRAG: Knowledge Graph construction with Neo4j, Cypher queries
- Entity Extraction, Graph Traversal for multi-hop reasoning
- Microsoft GraphRAG framework — local vs global search
- Project: Enterprise Knowledge Base with GraphRAG
🤖 Module 3 — Multi-Agent Systems
- CrewAI: Agents, Tasks, Crews, Tools, Process types (sequential, hierarchical)
- AutoGen: Conversable Agents, GroupChat, Code Execution
- Phidata: Agent Teams, Storage, Memory, Toolkits
- Agent Communication Patterns: Supervisor, Peer-to-peer, Blackboard
- Project: AI Hiring Pipeline — JD Writer + Screener + Interviewer
🔌 Module 4 — MCP (Model Context Protocol)
- MCP Architecture: Servers, Clients, Tool Schemas, Resources
- Building custom MCP servers in Python
- Tool Contracts: Input/Output schema design, Validation
- Integrating MCP tools with LangGraph agents
- MCP in Claude, Cursor, and other AI assistants
⚙️ Module 5 — n8n Workflow Automation
- n8n Setup: Cloud vs Self-hosted, Interface overview
- Nodes: HTTP, Webhook, Email, Database, AI Model integrations
- Building AI-powered automation: Lead Gen, Content Pipeline, Support Bot
- Error Handling, Retry Logic, Scheduling
- Enterprise Workflow: Multi-step approval processes with AI decision nodes
