Jesse Hall distinguishes a fast individual model from a responsive complete conversation. In a speech-recognitionAutomatic speech recognition converts spoken language into text or structured linguistic output using signal processing and machine learning., language-model and speech-synthesisText-to-speech converts written text into spoken audio by predicting pronunciation, timing, prosody and a waveform or intermediate audio representation. pipeline, early errors propagate into later decisions. He recommends measuring time to first audible output and time to a usable first sentence rather than relying on first-byte or first-token numbers.
Jesse Hall demonstrates how asynchronous tool execution lets an agent keep talking while backend work proceeds. Turn detection also needs to distinguish a finished statement from a pause inside an amount or date. His latency targets and comparative turn-detection results are presented as use-case guidance and vendor-reported observations, not universal guarantees.
Jesse Hall recommends testing a complete agent with simulated callers who interrupt, change their minds, mumble and introduce background noise. EvaluationAgent evaluation tests whether an AI agent completes tasks correctly, consistently, and within its required boundaries. should inspect the resulting database state, required safeguards and unnecessary or invented tool callsAgent tool use lets an agent call bounded external capabilities so it can retrieve information or change state beyond generating a response.. Repeating identical scenarios across model stacks produces a benchmark for the actual workflow, while observed failures become regression casesAI regression testing reruns preserved cases to detect whether a model or agent update has broken behavior that previously worked..
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