📖Lessons
Introduction to AI Agents
Learn what AI agents are, how they work, and when to use them in your applications
Tool Calling and Function Execution
Learn how to give AI agents the ability to use tools and execute functions
The ReAct Pattern
Master the ReAct (Reasoning + Acting) pattern for building reliable AI agents
Memory and Context Management
Learn how to give AI agents memory and manage context across multiple interactions
Planning and Reasoning
Learn how agents create plans and reason through complex multi-step tasks
Multi-Agent Systems
Learn how multiple AI agents can collaborate to solve complex problems
Agent Frameworks
Learn to use popular agent frameworks like LangChain, LlamaIndex, and CrewAI
Workshop: Building Your First Agent
Build a complete AI agent from scratch with tools, memory, and error handling
Agent Evaluation and Testing
Learn how to test, evaluate, and improve AI agent performance systematically
Production Agent Systems
Deploy, monitor, and scale AI agents in production environments
🎯Missions
Debug the Broken Tool-Calling Loop
Nebula Corp's AI agent is stuck in an infinite loop. The agent is supposed to call a weather tool, parse the response, and return a final answer to the user — but it never stops looping. The tool gets called over and over, and the agent never produces a result. Find the bug in the tool-response parsing logic and fix the loop so the agent terminates correctly.
Optimize the Multi-Step Agent Plan
Nebula Corp's AI agent planner is generating bloated, inefficient plans for user requests. A simple task like 'Book a flight and hotel for next Friday' produces 12 steps when 5 would do — redundant lookups, unnecessary confirmations, and repeated tool calls are burning through tokens and latency. Refactor the planning function to produce leaner plans that eliminate redundant steps while still completing every required action.
🔧Workshops
Agent Task Planner
Create an agent that breaks down complex tasks into subtasks using tree-of-thoughts planning.
Function Calling Wrapper
Build a universal function calling wrapper that works across OpenAI, Anthropic, and local models.