AI Agents & Agentic Workflows — Build Autonomous AI Systems
Design, build and deploy autonomous AI agents using LangChain, CrewAI and the Anthropic MCP.
About this course
AI agents are the defining technology of 2026 — Gartner projects 40% of enterprise applications will embed task-specific agents by end of year. This hands-on course teaches you to build multi-agent systems that plan, use tools, remember context and execute complex workflows autonomously. Covers LangChain, LangGraph, CrewAI, the Anthropic Model Context Protocol, OpenAI Assistants, and production deployment patterns.
What you'll achieve
- Design single and multi-agent architectures for real business problems
- Build agents with tool use, memory, planning and reflection capabilities
- Implement RAG-powered agents with vector database integration
- Orchestrate multi-agent teams with CrewAI and LangGraph
- Deploy agents to production with monitoring and guardrails
- Apply the Anthropic Model Context Protocol (MCP) for tool integration
Curriculum
Module 1
AI Agents Fundamentals
What are agents? · ReAct pattern · Tool use · Memory types · Planning strategies
Module 2
LangChain & LangGraph Deep Dive
Chains vs agents · LangGraph state machines · Conditional edges · Human-in-the-loop
Module 3
Tool Integration & Function Calling
OpenAI function calling · Custom tools · Web search · Code execution · API tools
Module 4
Memory & Context Management
Short-term memory · Long-term memory · Vector stores · Episodic memory · Semantic search
Module 5
Multi-Agent Systems with CrewAI
Roles & personas · Task delegation · Agent collaboration · Crew orchestration
Module 6
Anthropic MCP & Claude Integration
Model Context Protocol · MCP servers · Claude tool use · Building MCP tools
Module 7
Production Deployment & Monitoring
LangSmith observability · Guardrails · Cost management · Error handling · Scaling patterns
Module 8
Capstone: Enterprise Agent System
Problem scoping · Architecture design · Build & test · Demo & documentation
Who this is for
- Software developers & backend engineers
- Data scientists moving into AI engineering
- Product managers building AI-powered features
- Enterprise teams automating workflows with AI
Tools & technologies
Prerequisites
- Python basics (functions, classes, loops)
- Familiarity with APIs and REST concepts
- Basic understanding of LLMs (ChatGPT-level)