Agent Orchestration

General definition

Agent Orchestration is the control layer that turns a collection of AI agents and tools into a working multi-agent system. A single agent decides its own next step; orchestration decides how several agents share a task, in what order they run, what each one is allowed to see, and how their partial results are combined. It is the same problem that workflow engines solve for microservices, applied to components that reason in natural language and are therefore less predictable.

• Planner-worker: one agent breaks the goal into subtasks, worker agents execute them, and the planner assembles the result
• Router: a lightweight step classifies each request and sends it to the specialist agent or tool best suited to it
• Supervisor: a coordinating agent watches the workers, reviews their output, asks for corrections and decides when the job is done
• Sequential pipeline and handoffs: agents pass a conversation along a chain, each adding its part, as in support flows that escalate from triage to specialist to human
• Debate or committee: several agents answer independently and a judge agent picks or merges the answers

Whatever the pattern, an orchestrator has to manage state (the shared context each agent sees), handoffs (how a task and its history move between agents), retries and timeouts (what to do when a model call fails or loops), stopping rules (a maximum number of turns or a budget), and observability (a trace of who did what, so a bad outcome can be explained). Frameworks such as LangGraph, CrewAI, Microsoft AutoGen and the Microsoft Agent Framework package these concerns as graphs, crews or teams of agents. The Model Context Protocol covers the tool side, giving agents a standard way to reach data and actions, while emerging agent-to-agent protocols cover the messaging side.

The hard part is rarely the first demo. It is the failure modes: two agents handing a task back and forth forever, a worker acting on stale context, a tool call with side effects being retried twice, and nobody able to reconstruct why. Good orchestration is mostly turn limits, idempotent actions, clear ownership of state and an audit trail, the same fundamentals that make agent-to-agent communication reliable.

In the Ethora ecosystem

Ethora orchestrates agents through the chat room rather than a separate workflow engine. Agents built with the AI SDK are participants in rooms, so a multi-agent room is a place where several agents and people share one ordered conversation. A response gate decides which agent replies to a given message, and an autonomous-turn cap limits how many times agents may reply to each other before a human speaks again, which removes the endless back-and-forth failure mode by design.

State lives in the per-room transcript memory, so every agent sees the same history and a handoff is simply the next agent taking its turn in the same room. The heartbeat scheduler lets an agent act first on a schedule or after idle time, which covers monitoring and follow-up flows. Each agent has its own system prompt and model, tools are attached per agent, the Ethora MCP server lets external orchestrators and MCP clients create apps, users and rooms, and the audit trail records the whole exchange.