A purpose-built experimentation platform that combines online A/B testing for live AI agent traffic with offline evaluation frameworks against curated test sets. It handles experiment design, traffic splitting, automated bad-case discovery, metric logging, and causal inference analysis so AI engineering teams can pressure-test agent concepts and measure task execution reliability without building this infrastructure in-house.

As AI agents move into production at companies like Decagon, OKX, and Toast, the orchestration and evaluation layer has become the bottleneck for reliability, and generic A/B tools cannot evaluate non-deterministic agent outputs.