Multi-Agent Orchestrator

Expert in multi-agent AI systems — workflow coordination, tool-using agents, human-in-the-loop patterns, agent communication protocols, and production deployment of agent architectures.

Platinum

v1.0.00 activationsAI & Machine LearningTechnologyexpert

SupaScore

86.1

Research Quality (15%)

8.5

Prompt Engineering (25%)

8.8

Practical Utility (15%)

8.6

Completeness (10%)

8.3

User Satisfaction (20%)

8.7

Decision Usefulness (15%)

8.5

Best for

▸Design ReAct agent workflows with tool-calling capabilities for complex research and analysis tasks
▸Implement LangGraph-based orchestration with checkpointing and human-in-the-loop approval nodes
▸Build AutoGen conversational teams where coding agents collaborate with reviewer and executor agents
▸Deploy production multi-agent systems with error recovery, observability, and cascading failure prevention
▸Create orchestrator-worker patterns for task decomposition across specialist agents with different tool access

What you'll get

●Detailed LangGraph workflow diagram with state machines, conditional routing, and human approval nodes, plus Python implementation code
●Multi-agent architecture specification with agent persona definitions, tool permission matrices, communication protocols, and error handling strategies
●Production deployment configuration with observability setup, agent performance monitoring, cost tracking, and rollback procedures for agent system updates

Not designed for ↓

×Single-agent applications that don't require coordination between multiple AI systems
×Simple chatbot implementations or basic prompt engineering without agent orchestration
×Multi-agent systems in gaming or simulation environments (focuses on LLM-based agents)
×Agent systems without tool integration or external API interactions

Expects

Complex task descriptions requiring multiple capabilities, existing tool APIs or databases to integrate, and clear success criteria for agent coordination outcomes.

Returns

Complete multi-agent architecture specifications with agent roles, communication protocols, state management patterns, tool integrations, and production deployment configurations.

Evidence Policy

Standard: no explicit evidence policy.

multi-agentagent-orchestrationlanggraphautogentool-usehuman-in-the-loop

Research Foundation: 6 sources (2 paper, 3 official docs, 1 web)

This skill was developed through independent research and synthesis. SupaSkills is not affiliated with or endorsed by any cited author or organisation.

Version History

v1.0.02/15/2026

Initial version

Prerequisites

Use these skills first for best results.

Prompt Engineering StrategistGold

Works well with

AI Agent Framework ArchitectPlatinum Human-in-the-Loop Agent DesignerPlatinum LLM Observability EngineerGold Tool-Using Agent DesignerGold

Need more depth?

Specialist skills that go deeper in areas this skill touches.

Distributed Tracing EngineerGold Kubernetes Operations AdvisorGold Event-Driven Architecture DesignerGold

Common Workflows

Production Agent System Deployment

Design multi-agent architecture, implement tool integrations, add monitoring and observability, then deploy with orchestration

multi-agent-orchestrator→Tool-Using Agent Designer→LLM Observability Engineer→Kubernetes Operations Advisor

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