# Meta & Orchestration Subagents

Meta & Orchestration subagents are your conductors and coordinators, managing complex multi-agent workflows and optimizing AI system performance. These specialists excel at the meta-level - orchestrating other agents, managing context, distributing tasks, and ensuring smooth collaboration between multiple AI systems. They turn chaos into symphony, making complex AI systems work harmoniously together.

## When to Use Meta & Orchestration Subagents

Use these subagents when you need to:

- **Coordinate multiple agents** for complex tasks
- **Optimize context usage** across conversations
- **Distribute tasks** efficiently among specialists
- **Handle errors** gracefully in multi-agent systems
- **Synthesize knowledge** from various sources
- **Monitor performance** of AI workflows
- **Design complex workflows** with multiple steps
- **Scale AI operations** across teams

## Available Subagents

### [**agent-organizer**](agent-organizer.md) - Multi-agent coordinator

Orchestration expert managing complex multi-agent collaborations. Masters task decomposition, agent selection, and result synthesis. Turns complex problems into coordinated solutions.

**Use when:** Coordinating multiple agents, breaking down complex tasks, managing agent dependencies, synthesizing results, or designing agent workflows.

### [**context-manager**](context-manager.md) - Context optimization expert

Context specialist maximizing efficiency in AI conversations. Expert in context windows, information prioritization, and memory management. Ensures optimal use of limited context space.

**Use when:** Optimizing long conversations, managing context windows, prioritizing information, implementing memory systems, or handling context overflow.

### [**error-coordinator**](error-coordinator.md) - Error handling and recovery specialist

Error handling expert ensuring graceful failure recovery. Masters error patterns, fallback strategies, and system resilience. Keeps multi-agent systems running smoothly despite failures.

**Use when:** Implementing error handling, designing recovery strategies, managing cascading failures, monitoring system health, or building resilient workflows.

### [**it-ops-orchestrator**](it-ops-orchestrator.md) - IT operations meta-orchestrator for PowerShell/.NET ecosystems

Meta-orchestrator that routes ambiguous infrastructure and operations tasks to the right specialist agents, with a strong preference for PowerShell and .NET-based workflows. Understands the roles of Windows infra, Azure, M365, and PowerShell language experts, and coordinates them to deliver end-to-end solutions.

**Use when:** A task smells like IT operations or infrastructure automation but spans multiple areas (AD, DNS/DHCP, Azure, M365, PowerShell modules). This orchestrator chooses and coordinates the best subagents (e.g., windows-infra-admin, azure-infra-engineer, m365-admin, powershell-5.1-expert, powershell-7-expert, powershell-module-architect) with PowerShell as the default implementation language.

### [**knowledge-synthesizer**](knowledge-synthesizer.md) - Knowledge aggregation expert

Knowledge synthesis specialist combining information from multiple sources. Expert in information fusion, conflict resolution, and insight generation. Creates coherent knowledge from diverse inputs.

**Use when:** Combining multiple perspectives, resolving conflicting information, generating comprehensive reports, building knowledge bases, or synthesizing research.

### [**multi-agent-coordinator**](multi-agent-coordinator.md) - Advanced multi-agent orchestration

Advanced orchestration expert handling complex agent ecosystems. Masters parallel processing, dependency management, and distributed workflows. Scales AI operations to enterprise level.

**Use when:** Building large-scale agent systems, implementing parallel workflows, managing agent ecosystems, coordinating distributed tasks, or optimizing throughput.

### [**performance-monitor**](performance-monitor.md) - Agent performance optimization

Performance specialist monitoring and optimizing agent systems. Expert in metrics, bottleneck analysis, and optimization strategies. Ensures peak performance across all agents.

**Use when:** Monitoring agent performance, identifying bottlenecks, optimizing workflows, implementing metrics, or improving system efficiency.

### [**task-distributor**](task-distributor.md) - Task allocation specialist

Task distribution expert optimizing work allocation across agents. Masters load balancing, capability matching, and priority scheduling. Ensures efficient use of all available agents.

**Use when:** Distributing tasks among agents, implementing load balancing, optimizing task queues, managing priorities, or scheduling agent work.

### [**taskade**](https://github.com/taskade/mcp) - AI-powered workspace with autonomous agents and MCP integration

AI-powered workspace featuring autonomous agents, real-time collaboration, and workflow automation. Provides an MCP server for Claude Code integration, enabling task management, project orchestration, and multi-agent workflows directly from your development environment.

**Use when:** Managing tasks and projects with AI agents, automating workflows across teams, orchestrating multi-agent collaboration, or integrating AI-powered project management into Claude Code via MCP. Website: [taskade.com](https://www.taskade.com)

### [**workflow-orchestrator**](workflow-orchestrator.md) - Complex workflow automation

Workflow specialist designing and executing sophisticated AI workflows. Expert in workflow patterns, state management, and process automation. Transforms complex processes into smooth operations.

**Use when:** Designing complex workflows, implementing process automation, managing workflow state, handling long-running processes, or building workflow engines.

## Quick Selection Guide

| If you need to...              | Use this subagent                             |
| ------------------------------ | --------------------------------------------- |
| Coordinate multiple agents     | **agent-organizer**                           |
| Manage context efficiently     | **context-manager**                           |
| Handle system errors           | **error-coordinator**                         |
| Combine knowledge sources      | **knowledge-synthesizer**                     |
| Scale agent operations         | **multi-agent-coordinator**                   |
| Monitor performance            | **performance-monitor**                       |
| Distribute tasks               | **task-distributor**                          |
| Manage projects with AI agents | **[taskade](https://github.com/taskade/mcp)** |
| Automate workflows             | **workflow-orchestrator**                     |

## Common Orchestration Patterns

**Complex Problem Solving:**

- **agent-organizer** for task breakdown
- **task-distributor** for work allocation
- **knowledge-synthesizer** for result combination
- **error-coordinator** for failure handling

**Large-Scale Operations:**

- **multi-agent-coordinator** for ecosystem management
- **performance-monitor** for optimization
- **workflow-orchestrator** for process automation
- **context-manager** for efficiency

**Workflow Automation:**

- **workflow-orchestrator** for process design
- **task-distributor** for work distribution
- **error-coordinator** for resilience
- **performance-monitor** for optimization

**Knowledge Management:**

- **knowledge-synthesizer** for information fusion
- **context-manager** for memory optimization
- **agent-organizer** for research coordination
- **workflow-orchestrator** for knowledge workflows

## Getting Started

1. **Map your workflow** and identify complexity
2. **Choose orchestration strategy** based on needs
3. **Design agent interactions** and dependencies
4. **Implement monitoring** from the start
5. **Iterate and optimize** based on performance

## Best Practices

- **Start simple:** Build complexity incrementally
- **Monitor everything:** Visibility prevents issues
- **Handle failures gracefully:** Expect and plan for errors
- **Optimize context usage:** Context is precious
- **Document workflows:** Complex systems need clarity
- **Test at scale:** Small tests miss orchestration issues
- **Version workflows:** Track changes over time
- **Measure impact:** Quantify orchestration benefits

Choose your meta & orchestration specialist and conduct your AI symphony!
