Build AI Agents: From Fundamentals to Production
Architect autonomous multi-agent systems, custom tool-calling routines, and production RAG pipelines.
Go from introductory LLM usage to building production-ready autonomous AI agents that think, utilize tools, preserve memory across sessions, and collaborate in teams. Master LangChain, LangGraph StateGraph architectures, custom tool binding with Model Context Protocol (MCP), vector retrieval pipelines, and production evaluation suites.
STRUCTURED CURRICULUM BREAKDOWN
Foundations & Core Concepts
LLM Fundamentals, Agent Architecture, ReAct Pattern, n8n tool
Agentic Workflows and Patterns
Prompt Chaining, Parallelization, Routing, Reflection
RAG & Knowledge Systems
RAG Architecture, Vector Database, Build RAG systems, Fine-Tuning
Tools and MCP
Tool Schema, Tool Calling, Function Calling, MCP Architecture
Memory & Skills
Memory Fundamentals, Types of Memory, Skills Usage
LangChain Deep Dive
Architecture, LCEL & Runnables, End-to-end RAG Pipeline, AgentExecutor, Custom Tools
LangGraph & Core Concepts
StateGraph, Nodes & Edges, HITL routing, Supervisor-Workers
Multi-Agent Systems
Topology, Delegation, Parallel Execution, A2A-style Communication
Monitoring, Evaluation & Observability
LangSmith Tracing, Evaluation, Token Usage Agent Debugging, Guardrails, Prompt Injection defense
Production Deployment & Setup
Google Cloud Deployment, Azure Microsoft Foundry