# Clone Fan-Out 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.

- **Category**: Agentic AI
- **Subcategory**: Planning & Control Flow
- **Canonical URL**: https://designpattern.fyi/patterns/clone_fan_out_research/

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## Description
**Intent**: 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.
**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.
**Solution**: - A driver computes the input partition (one slice per clone) and allocates N isolated sandboxes so the clones cannot interfere with one another. - N instances of the same agent are launched 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 via 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.


## Use Cases
- The job naturally partitions into many independent units that each need full agent capability.
- Isolated sandboxes are available so clones cannot interfere.
- An aggregator (vote, rank, dedup, or synthesis) can produce one answer from N structured outputs.





## Trade-offs


### Advantages

- Wide-coverage jobs scale linearly with sandbox count.

- Identical clones simplify reasoning about per-agent quality.

- No inter-clone coordination means no message-passing failure modes.

- Isolation prevents one clone''s failure from poisoning others.




### Considerations & Drawbacks

- Cost scales linearly with N; budgets must be explicit.

- Aggregation quality caps overall quality; a weak aggregator wastes the fan-out.

- Identical clones cannot specialise to harder slices.

- Without strict spawn bounds this collapses into Unbounded Subagent Spawn.







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**Reference**: [Original Source](https://www.agentpatternscatalog.org/patterns/clone-fan-out-research/)