# Multivariate Testing (in AI)

Multivariate testing in the context of AI Agents is the controlled, simultaneous evaluation of multiple agent configurations — different LLM prompts, guardrail settings, routing logic, knowledge bases, or foundation models — to determine which combination delivers the best outcomes on defined metrics such as containment rate, resolution accuracy, or customer satisfaction. Unlike simple A/B testing, multivariate testing varies multiple dimensions at once, enabling faster identification of optimal configurations. NiCE Cognigy introduced embedded multivariate testing at Nexus 2026, allowing enterprises to simulate large-scale interactions before release and make evidence-based configuration decisions with statistical confidence.

For enterprise teams, Multivariate Testing (in AI) matters because real-world outcomes depend on how the capability is integrated, governed, and measured — not just on the underlying technology. Multivariate testing in the context of AI Agents is the controlled, simultaneous evaluation of multiple agent configurations — different LLM prompts, guardrail settings, routing logic, knowledge bases, or foundation models — to determine which combination delivers the best outcomes on defined metrics such as containment rate, resolution accuracy, or customer satisfaction.

## Key Points

- Simultaneously tests multiple AI Agent configurations across different dimensions
- Variables include: LLM prompts, guardrails, routing logic, knowledge bases, and models
- More powerful than A/B testing — identifies optimal combinations faster
- Enables pre-release simulation of large-scale interactions before production deployment
- Introduced by NiCE Cognigy at Nexus 2026 as a native platform capability

### See how it works in action

- [Watch the demo](https://www.youtube.com/watch?v=U87psHIQF3w)
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