Testing the Agents That Look You in the Eye [Testμ 2026]

TestMu AI engineering leaders Sai Krishna and Srinivasan Sekar presented an approach to testing customer-facing video AI agents by automating the human participant in live calls rather than relying on manual testers. Their system creates configurable “test candidates” with distinct personas, avatars, languages, dialects, behaviors, and memories, allowing repeated scenarios across diverse participants through joinable WebRTC links without SDKs, code changes, or test hooks. Because video agents must handle facial expressions, lip sync, audio, sentiment, turn-taking, and long-term context in addition to spoken language, evaluation extends beyond transcripts to assess full recordings, multi-turn conversational quality, goal completion, avatar presentation, and criteria-supported evidence. The presentation highlighted difficult technical issues including real-time latency, lip synchronization, distinguishing low audio levels from silence, interruptions and pauses, and memory-based red teaming for multi-round interviews. The platform generates scenarios from supplied context, supports user-defined validation criteria and personas, runs tests through HyperExecute, and is expanding toward native meeting apps, noisy environments, multi-person detection, and additional visual-attention metrics, while acknowledging that sign-language support remains a gap.

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