II · Planning & Control FlowExperimental·

Clone Fan-Out Research

also known as 通用副本扇出, Wide Research, Identical-Worker Fan-Out, Manus Wide Research

Spawn 100 or more identical, full-capability agent instances in parallel — each a complete general agent rather than a role-specialised worker — and aggregate their independent outputs into a single answer.

This pattern helps complete certain larger patterns —

Context

A team needs an agent to do a wide-coverage job — compare a long list of candidate libraries, scan a hundred different sources for the same kind of information, or sample many independent strategies for the same problem. Each individual unit of work is too large for a stripped-down worker prompt but small enough that a full general agent can finish it on its own. The infrastructure can hand each instance its own isolated environment, such as a sandbox virtual machine or a separate working copy of the codebase.

Problem

The usual orchestrator-workers pattern assumes specialisation: the orchestrator decomposes the job by role and hands each piece to a worker with a different skill. Many wide-coverage jobs are not role-decomposable at all — every unit needs the same full agent capability, just over a different slice of input. Inventing fake roles wastes the orchestrator's effort and produces inconsistent worker quality. Spawning hundreds of clones without isolation or an aggregation strategy collapses into the unbounded-subagent-spawn anti-pattern.

Forces

  • Wide coverage demands high parallelism, but parallel agents collide if they share state.
  • Each unit of work needs full agent capability, not a stripped-down worker.
  • Aggregation must reconcile many independent outputs without an O(N²) comparison.
  • Spawn cost and per-agent isolation cost grow linearly with N.

Example

A user asks an agent to compare 200 candidate libraries against five evaluation criteria. The driver partitions the list into 200 slices and spawns 200 identical agents, each in its own sandbox VM, each tasked with evaluating one library and emitting a structured row. After all clones finish, an aggregator pass ranks the rows and synthesises a shortlist. None of the clones talked to each other; the fan-out is bounded by the declared N=200.

Diagram

Solution

Therefore:

A driver computes the input partition (one slice per clone), allocates N isolated sandboxes (e.g. VMs or worktrees) so the clones cannot interfere with one another, and launches N instances of the same agent with the same system prompt and tools — only the input slice differs. Each clone runs to completion independently and writes a structured result to a shared collection bucket. A separate aggregator pass (LLM or deterministic) consolidates results — voting, ranking, deduplication, or synthesis. The clones never communicate; aggregation is one-shot at the end. N is bounded by a declared budget and the available sandbox pool, not by the agent's own discretion.

What this pattern forbids. The driver must declare N up front; the agent itself cannot decide to spawn more clones recursively; clones must run in isolated sandboxes with no shared mutable state; results must be aggregated in a single declared pass, not by inter-clone chatter.

The smaller patterns that complete this one —

  • usesSubagent Isolation★— Run subagents in isolated workspaces so their writes do not collide and parallelism is safe.

And the patterns that stand alongside it, or against it —

  • alternative-toOrchestrator-Workers★★— An orchestrator dynamically breaks a task into subtasks at runtime and delegates each to a worker LLM, then synthesises results.
  • conflicts-withUnbounded Subagent Spawn✕— Anti-pattern: a supervisor or orchestrator spawns sub-agents that can themselves spawn sub-agents without a global cap.
  • alternative-toLead Researcher★★— A lead agent writes a research plan and dispatches parallel sub-agents that fan out for breadth-first information gathering, then merges results.
  • alternative-toRole-Typed Subagents✕— Anti-pattern: pre-allocate roles (manager, coder, designer, researcher) across a fixed set of typed sub-agents and route tasks to them by role label.
  • complementsQuery-Decomposition Agent★★— An agent whose explicit job is to split an incoming user query into smaller independent sub-queries that can be answered sequentially or in parallel, then merge results.

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Used in frameworks

References

Provenance