Proceedings of the Raptors Conference

Abstract

Artificial intelligence is rapidly becoming operational infrastructure within enterprise software. AI agents now power chatbots, voice assistants, and automated telephony systems that interpret intent, generate responses, and execute workflows across customer service platforms. These systems operate probabilistically: identical prompts can produce different yet contextually valid outputs, challenging testing built on deterministic input-output validation.

This presentation examines how test architecture can evolve into a governance layer for non-deterministic AI systems. Conventional regression testing validates a single expected response, whereas AI requires distribution-based behavioral evaluation. I introduce the Scenario-Driven Behavioral Evaluation Framework (SBEF), an architectural testing approach executing hundreds of interaction scenarios across chat, voice, and telephony channels, including ambiguous prompts, adversarial inputs, and multi-turn conversations that expose hallucination, contextual breakdown, bias signals, and policy violations.

SBEF measures AI behavior using metrics including task-completion rate, unsupported-claim frequency, policy-violation rate, contextual retention, and interaction efficiency. Repeated execution across scenario sets produces behavioral distributions that let engineers define reliability and safety thresholds. The talk also covers observability pipelines capturing traces and evaluation scores to detect drift and coherence degradation, helping organizations move from deterministic validation toward continuous governance for AI systems in open-ended environments.