WBF AcademyWBF Academy
AI Agents Mastery
Intermediate AI

AI Agents Mastery

Build production-grade AI agents from first principles. Master the ReAct loop, tool use, memory, planning, RAG, and multi-agent orchestration. Code examples use Anthropic's Claude SDK; patterns port to any LLM provider. Frameworks covered: LangGraph, CrewAI, AutoGen.

5 modules 15 lessons 7 h 350 questions
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Course content

1 Foundations 3 lessons · 20 min
1 What is an AI Agent 6:16 2 The ReAct Loop 6:26 3 Building Your First Agent with Anthropic SDK 6:59
Practice · 0 of 70 questions Practice →
2 Tools & Memory 3 lessons · 20 min
4 Tool Calling Deep Dive 6:26 5 Short-term Memory 6:28 6 Long-term Memory with Vector Stores 6:47
Practice · 0 of 70 questions Practice →
3 Planning & RAG 3 lessons · 19 min
7 Task Decomposition with Planner-Executor 6:07 8 RAG for Agents 6:19 9 Self-Correction with Reflection 6:15
Practice · 0 of 70 questions Practice →
4 Multi-Agent 3 lessons · 20 min
10 Multi-Agent Patterns 6:19 11 LangGraph Tour 6:33 12 CrewAI and AutoGen Compared 6:59
Practice · 0 of 70 questions Practice →
5 Production 3 lessons · 21 min
13 Evals and Observability 6:22 14 Guardrails and Safety 7:03 15 Deploy and Cost Control 7:06
Practice · 0 of 70 questions Practice →

Tips

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MultiAgent Agents talk through state, not chat A shared object beats free-form messages Performance Cache the boilerplate, pay for the diff Prompt caching cuts cost up to 90% Production Route by request, not by hope Cheap model for easy, big model for hard Evals Build the eval set before the agent No dataset = no progress, only opinions