AI Agents Tech Crash Course From Concept To Deployment

In this section, you will walk through the process of building your first AI agent using LangChain and LangGraph. Using a playful scenario involving a squirrel navigating hazards to retrieve a golden acorn, you will understand how to create nodes, define prompts, and structure a basic agent workflow. This foundational agent will prove how LLMs can assess context, make decisions, and generate actions within a minimal agentic system.

In this section, you will take your agent-building skills to the next level by implementing conditional logic within LangGraph. You'll learn how to structure workflows with branching paths, validate agent responses, handle retries, and generate dynamic reports. This lecture introduces key concepts like nodes, edges, and control flow to build more intelligent, fault-tolerant AI systems.

This section introduces Crew AI, a high-level framework for quickly setting up and managing AI agents. Students will learn how to define agents using structured roles, goals, and backstories, bind them to tools and language models, and organize their execution using tasks. By the end of the lesson, you’ll have created a working Crew AI agent and understand how Crew simplifies complex agent orchestration compared to low-level frameworks like LangChain.

In this final section, you will learn how to scale from a single-agent setup to a fully operational multi-agent system. You'll define specialized agents with distinct roles and toolkits, coordinate your tasks, and run them sequentially. The lecture also introduces real-world integration using Claude Desktop and the Model Context Protocol (MCP), showing how to connect agents with dynamic tools and external interfaces. By the end, learners will have a full view of how AI agents can collaborate and operate in more realistic, flexible environments.