Claude API for Multi-Agent Orchestration

Multi-agent orchestration means coordinating several Claude-powered agents — each with its own role, prompt, and sometimes its own tools — to complete a task that a single call can't handle cleanly. The Claude API doesn't have a built-in "multi-agent" mode, but it gives you everything needed to build one: separate system prompts per agent, tool use for structured handoffs, and streaming for responsive orchestration. The architecture lives in your application layer, not in the API itself.

If you're searching for "claude api for multi agent orchestration," you're probably past the single-agent chatbot stage and need a planner agent delegating to specialist agents, or several agents reviewing each other's output before a final answer ships. This article covers the practical patterns: how to structure the calls, how to pass state between agents, how to route tool calls to the right agent, and how to keep the whole thing observable and cost-controlled.

The core orchestration patterns

Most multi-agent systems built on Claude fall into one of three shapes.

Sequential pipeline. Agent A produces output, Agent B refines or validates it, Agent C formats the final response. Each stage is a separate messages call with its own system prompt. This is the simplest pattern and the easiest to debug because failures are isolated to one stage.

Orchestrator-worker. A single "planner" agent decides which specialist to call next, based on the task. The planner's output is structured (often via tool use) so your code can route the request programmatically rather than parsing free text.

Parallel fan-out / fan-in. Several agents run concurrently on different slices of the same problem (e.g., summarizing different document chunks, or generating competing answers for a judge agent to pick between), and a final step merges results.

In practice, production systems mix these — a sequential pipeline where one stage is itself a parallel fan-out.

Using tool use to route between agents

The cleanest way to let one Claude call decide what happens next is to define the available agents as tools, rather than asking the model to say "call the research agent" in plain text.

{
  "name": "delegate_to_agent",
  "description": "Hand off a subtask to a specialist agent.",
  "input_schema": {
    "type": "object",
    "properties": {
      "agent": { "type": "string", "enum": ["researcher", "writer", "critic"] },
      "task": { "type": "string" }
    },
    "required": ["agent", "task"]
  }
}

When the orchestrator agent calls delegate_to_agent, your code catches the tool call, dispatches a fresh messages request to the named agent with its own system prompt, and feeds the result back as a tool_result block. This keeps the routing logic deterministic and testable — you're not relying on string matching inside a model's reply. See /docs/tools for the mechanics of defining and handling tool calls, and /docs/messages for the request/response shape each agent call follows.

Managing state between agents

The hardest part of orchestration isn't calling Claude multiple times — it's deciding what context each agent actually needs. Passing the entire conversation history to every agent wastes tokens and often confuses specialist agents with irrelevant context from other stages.

A workable approach:

• Keep a shared task state object (plain JSON) that accumulates structured outputs from each agent — not raw transcripts.
• Give each agent only the slice of state relevant to its job, formatted into its system or user message.
• Use a final "synthesizer" agent that receives the accumulated state, not the full history of every intermediate call.

const state = { brief: null, research: null, draft: null };

state.research = await callAgent("researcher", { brief: state.brief });
state.draft = await callAgent("writer", { research: state.research });
const final = await callAgent("critic", { draft: state.draft });

This also makes retries cheap: if the writer agent fails, you re-run just that step with the existing research instead of redoing the whole chain.

Keeping orchestration observable and under control

Multi-agent systems multiply your API traffic — a single user request might trigger five or ten Claude calls. Two things matter here that are easy to overlook early on:

Per-agent key scoping. Give each agent (or at least each stage) its own API key so you can see which agent is driving cost and latency, and so a misbehaving agent can be rate-limited or disabled without affecting the others. If you're running this on SubToAPI, you can issue separate sub_live_ keys per agent or per team under one account, and the dashboard breaks down usage by key — useful when a critic agent starts looping or a researcher agent starts over-fetching.

Streaming for the user-facing leg only. Intermediate agent calls (planner, researcher, critic) rarely need to stream — you want the complete structured output before moving to the next stage. Reserve streaming for the final response the user actually sees, which keeps your orchestration code simpler and avoids juggling partial tool-call JSON mid-pipeline. See /docs/streaming for how streamed responses are structured if you do need it for a long-running final stage.

If you're evaluating whether to build this orchestration layer on raw Claude access or through a managed API layer, the orchestration logic itself (routing, state, retries) is the same either way — what changes is how much plumbing you need for keys, usage tracking, and team access. SubToAPI turns your existing Claude access into an HTTPS API with per-key usage metadata and seats, which is handy once you have multiple agents and multiple people on a team hitting the same underlying account. Check /pricing or start from /docs/quickstart if you want the orchestration layer without building key management yourself.

A minimal orchestrator loop

async function orchestrate(task) {
  const plan = await callAgent("planner", { task });

  const results = [];
  for (const step of plan.steps) {
    results.push(await callAgent(step.agent, { task: step.input }));
  }

  return callAgent("synthesizer", { task, results });
}

Each callAgent wraps a messages request with the system prompt for that role. This is deliberately plain — resist the urge to add a generic "agent framework" before you've run a few real pipelines. Most orchestration bugs come from ambiguous handoffs, not from missing abstraction.

Common mistakes to avoid

• One giant system prompt trying to be every agent. Split roles into separate calls even if it costs more tokens — it's easier to debug and tune independently.
• Free-text routing instead of tool use. Parsing "I think we should call the research agent next" out of a response is fragile. Use structured tool calls for routing decisions.
• No circuit breaker. Orchestrator-worker loops can recurse indefinitely if a planner keeps delegating. Cap the number of hops per task.
• Sharing one API key across all agents in production. It works in a prototype, but you lose per-agent cost visibility the moment something misbehaves.

Questions

Does the Claude API have a native multi-agent mode? No. Claude exposes a standard messages/tool-use API; multi-agent orchestration (routing, state, handoffs between roles) is built in your application code on top of that.

Should each agent run as a separate API call? Yes, in nearly all cases. Separate calls with distinct system prompts per role are more reliable and easier to debug than one call trying to simulate multiple personas.

How do I control cost when running many agent calls per request? Scope keys per agent or team, track token usage per key, cap orchestration hops, and avoid passing full conversation history to every agent — pass only the structured state each one needs.