All agents communicate through the Orchestrator. No direct agent-to-agent traffic.
Agent Topology
Message Queue
Orchestrator→Researchertask✓ delivered
Search for recent ML papers on attention mechanisms
Orchestrator→Codertask✓ delivered
Implement a simple transformer block in PyTorch
Researcher→Orchestratorresult✓ delivered
Found 12 papers. Top: FlashAttention-2, MLA, MQA
Orchestrator→Reviewertask⏳ pending
Review the transformer code for correctness
Coder→Orchestratorresult⏳ pending
transformer.py ready. 87 lines, tested on MNIST.
Pattern
Agents
Orchestrator
Researcher
Coder
Reviewer
pending2
delivered3
failed0
Click a message to highlight the agents and edges involved in that communication.
Agent Communication Protocols - Interactive Visualization
Multi-agent systems rely on structured communication protocols to coordinate work. The three primary patterns are hub-and-spoke (all messages routed through an orchestrator), peer-to-peer (agents communicate directly), and broadcast (one agent sends to all simultaneously). Each pattern has different latency, reliability, and debuggability tradeoffs. Understanding message types - task, result, feedback, error - and their delivery status is essential for building robust agentic pipelines.
Hub-and-spoke centralizes control through an orchestrator, simplifying monitoring but creating a bottleneck
Peer-to-peer enables faster direct communication but makes message tracing harder
Broadcast efficiently distributes updates to all agents but cannot target individual recipients
Message status tracking (pending / delivered / failed) is critical for fault-tolerant multi-agent coordination
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.