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

=Ë Available Subagents

agent-organizer - 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 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 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.

knowledge-synthesizer - 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 - 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 - 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 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.

workflow-orchestrator - 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
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

Map your workflow and identify complexity
Choose orchestration strategy based on needs
Design agent interactions and dependencies
Implement monitoring from the start
Iterate and optimize based on performance

=Ú 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!