AI agent orchestration is just the coordination flow of one or more agents. You decide which ones run, in what order, how they pick the next step, and how control and context move between them.
Two patterns dominate the implementation space. First, handoffs. A specialist agent takes over and owns the next response. Second, manager-style orchestration. A manager agent keeps control of the user-facing reply while calling specialist agents as bounded helpers.
Frameworks describe this via routing, context propagation, and control-flow decisions.
The system runs on specific components.
The orchestrator or supervisor agent sits at the center. It receives requests, maintains state, manages flow, and delegates to sub-agents.
Specialized agents are domain experts. They execute tasks assigned by the orchestrator.
An agent registry acts as a directory. It manages and audits available agents, including capabilities and metadata.
Memory and context management provide persistent storage. This keeps conversation history and context for informed decisions.
Model Context Protocol (MCP) clients and servers standardize access to external tools, databases, or APIs.
Classification components route requests based on intent.
Tool-calling frameworks let agents perform actions beyond model reasoning.
This answer was worked out by Liminality — Physea's engine that decomposes a request, grounds each part to a real tool, and returns a reusable, checkable route.
Connect it over MCP: https://mcp.physea.ai/mcp · physea.ai