Apr 23
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One agent doing everything? Time to build a team. In this hands-on tutorial, you'll build a LangGraph system where two specialised agents collaborate — a research agent gathers live gold price data, and an analysis agent interprets it and generates market insights. A router decides which agent handles each question, and a second decision point determines whether analysis is needed after research. What we cover: - Why specialisation beats one agent doing everything - Three paths through one graph: data only, data + analysis, analysis only - System prompts that give the same LLM completely different behaviour - A keyword-based router that directs questions to the right agent - Shared state vs separate prompts — how agents communicate without seeing each other's instructions - Memory across agents — the analyst uses data the researcher collected New concepts: Multiple agent nodes, system prompts, router, sequential handoff This is Tutorial 5 in the AI Orchestration from Zero series. ← Tutorial 7 (Security Fundamentals): https://youtu.be/YMQsCpsiP7I ← Tutorial 6 (Custom Tools + APIs): https://youtu.be/kv-jN4NVmtg ← Tutorial 5 (Multi-Agent Basics): https://youtu.be/nx6HaySGOlc ← Tutorial 4 (State and Memory): https://youtu.be/5UQcHi538-c ← Tutorial 3 (Tool-Using Agent): https://youtu.be/4eUdFquzulU ← Tutorial 2 (Setup + Hello World): https://youtu.be/uGmhG_zSWOs ← Tutorial 1 (Concepts): https://youtu.be/EgIULrnjsL8 📂 Code on GitHub: https://github.com/lngo/ai-orchestration-from-zero/tree/main/tutorial-05 ⏱️ Timestamps: 0:00 Introduction 0:24 Why multiple agents? 1:07 Three paths through the graph 2:01 Four new concepts 2:40 Writing the multi-agent code 6:04 Running the demo — three paths in action 7:41 How system prompts create specialisation 8:25 Shared state vs separate prompts 9:05 Interactive demo 10:00 Key concepts 10:38 What's next: Tutorial 6 11:08 Closing This series covers all three approaches to AI orchestration: → Phase 1 (Tutorials 2–7): Developer-controlled orchestration with LangGraph → Phase 2 (Tutorials 8–9): Autonomous agents with Claude Code → Phase 3 (Tutorials 10–11): LLM-as-orchestrator — agent teams and hybrid architectures 🔔 Subscribe for the full series: @AIForYourWork #LangGraph #MultiAgent #AIOrchestration #SystemPrompts #ClaudeAPI #PythonTutorial #AIForYourWork #BuildMoreHireLess #AgentRouter #AIAgentTeam