Testing OpenAI Realtime Agents: A Practical Guide

OpenAI Realtime agent testing requires evaluating observable voice-session behavior rather than relying on transcripts, because its speech-to-speech architecture may not expose a reliable intermediate text record and cannot capture issues such as incorrect spoken digits or callers being talked over. Testing should separately verify tool-call execution, tool selection, argument accuracy, timing, spoken readbacks, required scripts, disclosures, and both server-side generation cancellation and client-side playback stopping during interruptions. OpenAI’s published gpt-realtime benchmarks show strong audio reasoning but comparatively low multi-turn instruction-following performance, supporting the need to test prompt changes and production-specific scenarios. Test coverage must also reflect the deployed transport, since WebRTC, WebSocket, and SIP introduce distinct risks such as playback buffering, reconnections, event ordering, phone-codec compression, jitter, and DTMF handling. Additional cases should address asynchronous tool responses, changing remote MCP tool surfaces, misleading image input, language changes, and classifier-terminated sessions, while CI should distinguish agent defects from transport failures, preserve audio for review, and run tests against the actual customer-facing channel.

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