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workflow

Orchestrate multi-agent systems with ensemble, pool, and retry patterns.

113|23|Updated Mar 23, 2026
Search Tags:#automation#workflow#concurrency#multi-agent#orchestration#subagent
One-click install
npx skills add https://github.com/opensage-agent/opensage-adk --skill workflow-opensage-agent
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: workflow
Source: https://github.com/opensage-agent/opensage-adk/tree/main/src/opensage/bash_tools/workflow
Command: npx skills add https://github.com/opensage-agent/opensage-adk --skill workflow-opensage-agent

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill addresses the complexity of managing multiple AI agents by providing standardized orchestration patterns that ensure reliable task execution, error recovery, and concurrency management.

Core Features & Use Cases

  • Ensemble Pattern: Run tasks across multiple models simultaneously to compare outputs and improve accuracy for high-stakes decisions.
  • Pool Pattern: Manage large batches of subtasks under strict concurrency limits to prevent rate-limiting and resource exhaustion.
  • Multi-Model Recovery: Automatically recover from stalled subagents by retrying with fresh context or switching to alternative models.

Quick Start

Use the workflow skill to ensemble three different models to verify the security of the provided code snippet.

Frequently Asked Questions about workflow

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I orchestrate multiple AI agents to handle complex task decomposition?▼

You can orchestrate multi-agent systems by applying standardized patterns for task decomposition, using ensemble, pool, and multi-model retry strategies to manage subagent lifecycles and ensure reliable execution.

What is the best way to manage concurrency limits for large batches of subtasks?▼

The pool pattern manages large batches of subtasks under strict concurrency limits, preventing rate-limiting and resource exhaustion while maintaining reliable multi-agent execution and error recovery.

Can I run tasks across multiple models simultaneously to compare outputs?▼

Yes, the ensemble pattern runs tasks across multiple models simultaneously to compare outputs, improving accuracy and reliability for high-stakes decisions during complex multi-agent task execution.

How does multi-agent error recovery work when a subagent stalls?▼

Multi-model recovery automatically handles stalled subagents by retrying with fresh context or switching to alternative models, ensuring continuous task execution and preventing workflow failure.

Do I need existing subagent tools to manage multi-agent concurrency and model selection?▼

Yes, this orchestration workflow requires integration with existing subagent tools to actively manage concurrency, model selection, and automated error recovery within your established environment.

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