Case Study: cubic reduces false positives by 51% with Inngest

How cubic reduced false positives by 51% by orchestrating their multi-agent system with Inngest

cubic, an AI SaaS company, faced challenges with its multi-agent code review system. The system was failing unexpectedly on larger codebases with limited observability, making issues difficult to diagnose. Seeking a serverless queuing solution, they needed better infrastructure for orchestration, observability, and to handle long-running processes.

By implementing Inngest, cubic gained a robust event-driven orchestration platform. Inngest provided the observability and step APIs needed to rebuild their architecture, transitioning from a single agent to a multi-agent system. This resulted in a 51% reduction in false positives and enabled them to merge pull requests four times faster. Inngest's features for parallel execution, idempotency, and flow control were essential to managing their scalable AI workflows.

cubic

Allis Yao

Co-Founder & CEO