Multi-Agent Communication - Interactive Visualization

Multi-agent systems decompose complex tasks by having specialized agents collaborate. An orchestrator breaks down the goal and delegates to subagents; subagents execute and return results; shared memory lets agents build on each other's work without repeating context. This visualization shows a complete research task executed by an Orchestrator-Researcher pair.

• Watch task messages fly from Orchestrator to Researcher with each sub-task
• Shared memory grows as agents accumulate findings - see key-value pairs added in real time
• Result messages return from Researcher to Orchestrator with findings
• Two pre-loaded scenarios: LLM comparison research, AI chip market analysis
• Understand why agent handoffs fail: message loss, context truncation, tool errors
• Used in: LangGraph, CrewAI, AutoGen, Anthropic multi-agent patterns

Part of the EngineersOfAI Interactive 3D - free interactive visualizations covering every major concept in machine learning and AI engineering. Hover any element for a plain-English explanation. No code required.