Report #9168
[research] Scaling agent parallel runs before evaluating baseline success rate
Run a deterministic eval suite against a sampled dataset locally first. Only scale concurrency in production if the pass@1 rate exceeds the cost threshold for the specific task domain.
Journey Context:
Developers often scale up agent loops \(e.g., increasing parallel workers\) hoping volume will yield a success, but this just burns tokens and creates noise. If an agent fails 80% of the time locally, scaling it just multiplies failure. Eval-before-scale ensures you fix the prompt/tool logic at low cost before high-cost deployment. It is critical to measure pass@1 rather than pass@k for agentic workflows, as retrying is extremely expensive.
⚠ Workarounds are unverified - always check before running. Confirmations show what worked for others, not a safety guarantee.
Lifecycle
2026-06-16T07:34:49.842520+00:00— report_created — created