A mechanism that controls task distribution, state management, and coordination flows among multiple AI agents.
## What is Agent Orchestration Agent orchestration is a mechanism for distributing tasks across multiple AI agents while managing the state and coordination flow of each agent. It resolves complex workflows that a single agent cannot handle alone by enabling collaboration among specialized agents. ### Why "Orchestration"? Just as an orchestra conductor controls when each instrument plays and at what tempo, an orchestrator manages "which agent," "when," and "in what order" each agent operates. For example, in a sales support workflow, processing is handed off sequentially: a research agent → a proposal drafting agent → a review agent. ### Major Frameworks Mastra, LangGraph, CrewAI, and others provide orchestration capabilities. Design patterns fall broadly into two categories: - **Sequential pipeline**: A serial model in which the output of Agent A becomes the input of Agent B. Predictable and easy to debug. - **Dynamic routing**: The next agent to be called is determined dynamically based on results. Flexible, but difficult to control. ### Integration with HITL In practice, rather than automating the entire process, it is recommended to design workflows that incorporate HITL (Human-in-the-Loop) at high-risk decision points. The orchestrator evaluates a confidence score and, when it falls below a threshold, routes the task to a human review queue — this sparse supervision model is a practical solution for balancing autonomy and safety.


An AI agent is an AI system that autonomously formulates plans toward given goals and executes tasks by invoking external tools.

A multi-agent system is an architecture in which multiple AI agents divide roles and coordinate with each other to accomplish a shared objective.

A2A (Agent-to-Agent Protocol) is a communication protocol that enables different AI agents to perform capability discovery, task delegation, and state synchronization, published by Google in April 2025.

What is Multi-Agent AI? From Design Patterns to Implementation and Operational Best Practices

Agent Skills are reusable instruction sets defined to enable AI agents to perform specific tasks or areas of expertise, functioning as modular units that extend the capabilities of an agent.