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Insights on voice AI evaluation
Should You Build a Graph-Based or Prompt-Based Voice Agent?
Graph-based vs prompt-based voice agents: a decision framework. Five questions, a clear matrix, and three hybrid patterns most teams actually ship.
Why Production Voice Agents Are Becoming Graphs, Not Prompts
Graph-based voice agents externalise control flow that prompt-based agents trust the LLM to handle. Why this is the architecture winning production deployments.
The ROI of AI Voice Agent Testing Isn't What You Think It Is
Cost-per-test is the wrong way to measure voice agent testing ROI. Six capabilities that change how teams ship voice AI — and what they're worth.
You don't read AI-generated code. Why are you listening to every call?
Nobody reads AI-generated code anymore. Tests became mandatory. Voice agents are next — eval-driven development is how reliable voice AI gets shipped.
AI Agent Testing vs Voice Agent Testing: What General Tools Miss for Voice
AI agent testing measures text outputs. Voice agent testing measures behaviour through an acoustic pipeline. Five failure categories general tools miss.
What is a voice agent? The complete guide
What is a voice agent? How it works, how it differs from IVR and chatbots, use cases by industry, and what production-ready deployment actually requires.
Voice agent stack: the complete guide
Voice agent stack guide: STT, LLM, TTS, orchestration, and telephony. Latency budget, cost per layer, build vs buy, and what to test before production.
Pipecat voice agent testing guide: what to check before going live
Test your Pipecat voice agent before going live. VAD, frame errors, transport, Flows state machine, and pipeline regression after every processor change.
Deepgram STT testing guide for voice agents: what the benchmarks don't tell you
Nova-3 benchmarks look clean. Production audio never is. Test your Deepgram STT for noise, accents, latency, and error compounding before going live.