cubic reduces false positives by 51% by orchestrating their multi-agent system with Inngest
cubic's initial single-agent AI code review system produced an influx of low-value comments and false positives, while the multi-step agentic system would fail or become stuck on larger codebases with limited observability making root-cause diagnosis difficult.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Pull Request triggers agents
A new Pull Request event triggers cubic's multi-agent review system.
Tools used
Inngest
Outcome
Transitioning to a multi-agent system orchestrated by Inngest resulted in a 51% reduction in false positives and enabled teams to merge Pull Requests 4x faster, while replacing manual log tagging with fine-grained, searchable traces.
What failed first
The original 'Single, Do-Everything Agent' design produced noisy, low-value review comments and false positives, and the serverless architecture caused intermittent timeouts that the system could not handle gracefully, with no async queuing layer to recover from failures.