AI Agents.
Autonomous AI agent systems — ReAct, tool-calling, memory, multi-agent orchestration, and production agent patterns.
Beginner
Start here — no prior experience needed
AI Agents Learning Roadmap
A structured path from building your first tool-using agent to orchestrating multi-agent systems that plan, remember, and self-correct.
AI Agents Projects
Projects that build real autonomous agents — from single-tool agents to multi-agent systems that plan, execute, and self-correct.
Introduction to AI Agents
Understand what AI agents are, how the ReAct pattern works, and build your first autonomous agent that plans and executes multi-step tasks.
Intermediate
For developers with core concepts down
AI Agent Planning Patterns
Give agents the ability to decompose complex goals into steps, choose tools strategically, and recover from failures.
AI Agent Memory Systems
Give agents persistent memory — short-term conversation context, long-term key-value storage, and semantic memory with vector search.
Advanced
Production-grade patterns for experienced engineers
Multi-Agent Systems
Build systems where multiple AI agents collaborate — orchestrators, subagents, parallel execution, and human-in-the-loop patterns.
RAG Agents
Build agents that combine retrieval-augmented generation with tool use — search, fetch, synthesize, and cite sources.
Agent Evaluation
Measure agent quality systematically — task completion rates, tool use efficiency, safety, and regression testing for agentic systems.
Autonomous Agent Workflows
Build agents that run long multi-step tasks autonomously — task decomposition, parallel execution, checkpointing, and graceful failure recovery.