Agent Orchestrator
System that coordinates multiple AI agents to work together on complex tasks, managing task distribution, parallel execution, and result synthesis. Key component in ASMR and other multi-agent systems.
About this tool
Overview
An Agent Orchestrator is a system that coordinates multiple AI agents to work together on complex tasks, managing their interactions, task distribution, and result synthesis.
Key Responsibilities
- Task Decomposition: Breaking complex queries into subtasks
- Agent Assignment: Routing subtasks to appropriate specialized agents
- Parallel Execution: Coordinating concurrent agent operations
- Result Synthesis: Combining outputs from multiple agents
- Error Handling: Managing failures and retries
- Resource Management: Optimizing agent utilization
Examples in Production
ASMR (Supermemory)
Deployes an agent orchestrator utilizing 3 parallel reader agents and 3 parallel search agents for memory retrieval.
Emergence AI
Uses a plan-execute-verify framework to orchestrate self-improving agents and dynamically created variant agents.
AG2/AutoGen
Provides orchestration patterns for multi-agent conversations and task execution.
Design Patterns
- Hierarchical: Master agent delegates to worker agents
- Peer-to-peer: Agents communicate directly
- Pipeline: Sequential agent execution
- Parallel: Concurrent agent execution with result merging
Challenges
- Agent coordination complexity
- State management across agents
- Handling conflicting outputs
- Performance optimization
- Cost management
2026 Trends
Agent orchestration is becoming standardized through frameworks like AG2, LangGraph, and CrewAI, with built-in orchestration primitives.
Pricing
Pattern/concept, implemented through various frameworks.
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