We benchmarked agent fanout against our SDK pipeline. The SDK ran 1,165% faster.
By The Verbiflow teamThere are two reasonable ways to run batch outbound work from a coding agent. You can have Claude Code write a pipeline using the Verbiflow SDK and run it as one job. Or you can have Claude Code dispatch a fleet of subagents and let each subagent do one slice. We ran the same task both ways: find every security certification claimed on the trust pages of 10 well-known SaaS companies, with the same SDK, model, and task. Here’s what we saw.
Wall clock
Retries needed
Same answer
What this benchmark actually shows
This is not an argument against agents or subagents. If you are analyzing one company, asking an agent to do it directly is probably fine. The setup cost of building a pipeline may not be worth it for a one-off task.
But outbound work is rarely one company. Once the same operation needs to run across 10, 100, or 1,000 accounts, the tradeoff flips. The pipeline cost gets paid once, then every additional company gets cheaper, faster, and more reliable.
The subagent version treated 10 companies like 10 separate jobs. The SDK pipeline treated them like one batch job.
The principle
The important part is that the SDK pipeline is not “non-AI.” It still uses LLM calls for extraction, classification, and reasoning where needed. The difference is that those LLM calls run inside a structured pipeline with concurrency, caching, retries, and shared state instead of inside 10 separate agent loops.
Use the agent to design and adjust the workflow. Use the SDK pipeline to run that workflow at scale, including the LLM steps.