Agent-to-Agent Testing is an AI-native quality framework that validates AI agent behavior across chat, voice, phone, and multimodal systems. It enables multi-agent scenario generation, autonomous synthetic user testing, unified quality scoring, behavioral and edge-case stress testing, AI-specific metrics, and compliance validation. Teams can detect hallucinations, bias, security risks, and escalation failures to ensure reliable, production-ready AI agents at scale.