What Is Agent Orchestration?

Agent orchestration is the coordination of multiple AI agents working toward a shared goal — assigning roles, sequencing tasks, sharing state, and resolving conflicts so agents complement each other instead of colliding. An orchestration layer tracks what every agent is doing, what is ready, and what is blocked, turning a set of independent agents into a working system.

The term gets used for everything from a script that calls two models in sequence to a fleet of autonomous coding agents shipping a production app. This post pins down the definition, the patterns that keep showing up in real systems, and what an orchestration layer actually has to provide — grounded in how coordination works over MCP.

Why agent orchestration exists

A single agent with a large context window can do a lot. Three walls show up once the work gets real. The first is context: no agent holds your full frontend, backend, database schema, and deploy state at once, so it works from a partial picture and guesses at the rest. The second is throughput: one agent does one thing at a time, and a strictly serial agent workflow turns a parallelizable build into a queue. The third is specialization: the agent tuned for backend migrations is not the agent you want reviewing CSS, and prompting one generalist to do everything degrades all of it.

The obvious response — run several agents at once — creates the exact problem orchestration solves. Two agents editing the same app without coordination duplicate work, disagree about API shapes, and overwrite each other. Concretely: a frontend agent writes a call to POST /orders/cancel while a backend agent renames the endpoint to PATCH /orders/{id}. Each did its job; the app is broken. Orchestration is the difference between several agents and a system.

Orchestration vs a single agent with tools

These two setups get blurred constantly, and the distinction matters. A single agent with tools — file editing, search, a couple of MCP servers — is an agent workflow: one decision-maker reasoning sequentially, with tools as its hands. Agent orchestration starts when there is more than one decision-maker, and the question changes from "what can this agent do?" to "who does what, in what order, and how do they avoid colliding?"

That shift adds requirements a single-agent setup never has: task assignment, shared visibility into work in flight, contracts between agents that own different parts of the system, and a way to detect conflicting output. Some of that can live in a dedicated orchestration agent — a coordinator that plans and dispatches. Most of it should live in infrastructure instead, because agents restart, lose context, and run in parallel; state that exists only inside one agent's context window evaporates with the session. Within one session, Claude Code subagents are the lightweight version of this delegation: focused workers that return results to a caller.

Agent orchestration patterns

Four agent orchestration patterns cover most real systems. They are not mutually exclusive — production setups usually combine two or three.

Orchestrator-worker (hub-and-spoke). One coordinator agent decomposes a goal into tasks, dispatches them to worker agents, and integrates the results. This is the pattern behind most "manager agent" demos. It is simple to reason about and easy to bottleneck: the orchestrator is a single point of failure, and its context window becomes the ceiling of the whole system.

Role-based pipeline. Work flows through specialists in a defined order — backend implements, frontend integrates, QA verifies, a human signs off. Each role has an explicit contract for what "done" means before the next role starts. AppHandoff covers this sequencing with blocking links and milestones: acceptance criteria define done for each ticket, and the validation stage holds the final close until the deploy is recorded.

Blackboard (shared state). Instead of agents messaging each other point-to-point, every agent reads and writes one shared store and reacts to changes in it. No agent needs to know the others exist — the state is the protocol. AppHandoff's shared Kanban for humans and bots is a blackboard: tickets move through backlog → up next → in progress → validation → live, and every agent and human reading the board sees the same truth.

Market (claim-based). Work is published as available and agents claim it, instead of a dispatcher assigning it. The system self-balances — faster agents pull more work — and there is no central coordinator to bottleneck. AppHandoff ships this through the ticket board: work is published as open tickets, an agent asks for its next ready ticket and claims it, naming the paths it will touch; while its claim's lease is live, another agent's claim on that ticket is refused.

Which pattern fits depends on the shape of the work. For AI-built apps — a frontend, a backend, a deploy pipeline, several agents and at least one human — the combination that holds up is a blackboard for state, blocking links and milestones for sequencing, and claims for assignment. That is the shape AppHandoff ships, and the agent orchestration overview walks through it end to end.

What an orchestration layer must provide

Whatever pattern you run on top, the layer underneath has the same job description. Four things are non-negotiable.

Shared state. Every agent and every human reads the same picture of what exists, what is in flight, and what is done. If that picture lives inside one agent's context, it dies with the session — and the next agent starts from zero.

Contracts. Agents on different sides of a boundary need a machine-readable agreement: the API spec, the database schema, which routes the frontend actually calls. With contracts, "the backend changed" is a detectable event instead of a runtime surprise.

Conflict visibility. Parallel work drifts. The layer should make interface changes explicit — contracts published by one side and confirmed by the other — so disagreements surface as tickets an agent can act on, before deploy, not after.

An audit trail. When four agents and two humans touch the same system, "what happened and who did it" must be answerable. Tickets, claims, and activity logs are the difference between a debuggable system and archaeology.

What this looks like in practice with MCP

The Model Context Protocol is the practical way to wire agents to an orchestration layer. An MCP server exposes tools that any spec-compliant client — Claude Code, Cursor, Codex, or Lovable via its MCP integration — can call. That makes the layer agent-agnostic: instead of building coordination into each agent, you point every agent at one server that holds the shared state.

AppHandoff is that server for AI-built apps: a hosted MCP server at https://api.apphandoff.com/mcp. Connect your GitHub repos and your agents: work lives as handoff tickets, interfaces live as agent-published contracts, and a merged PR with a closing line such as Closes HO-123 closes the ticket it resolves once its deploy is recorded — any other mention only links the merge. Handoff tickets, claims, and milestones give agents the blackboard; merge webhooks and deploy signals close the loop after the work ships. The tools are direct and typed — bootstrap to open a session, find and get to read the board, ticket to file and move work — and the whole server is nine tools, so the catalogue costs very little context.

For a concrete walkthrough of multiple agents coordinating on one app this way — who claims what, how claims hand off, where the human steps in — see how an MCP-coordinated AI agent team ships an app.

The short version

Agent orchestration is coordination, not magic: shared state, explicit roles, machine-readable contracts, and conflict detection, applied to more than one agent. Get those four right and the pattern on top — hub-and-spoke, pipeline, blackboard, market — is a tuning choice. Get them wrong and adding agents just adds collisions faster.