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Tracking AI product quality over time — drift, degradation, and improvement.
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Baseado na classificação ocupacional SOC
Proactively identifying failure modes, misuse, and unintended consequences.
Managing shared context, memory, and state across multiple agents.
Coordinating text, image, voice, and tool-use modalities in a single interaction.
Helping users form warranted trust in the AI — neither overtrust nor undertrust — through deliberate confidence and source signalling.
Reading user emotional state from text signals — caps, punctuation density, repetition, latency — and adapting before the user disengages.
Designing review workflows to surface and mitigate bias in AI outputs.
| name | longitudinal-measurement |
| description | Tracking AI product quality over time — drift, degradation, and improvement. |
AI products change over time — models get updated, usage patterns shift, and quality can drift without anyone noticing. Longitudinal measurement is how you track quality across time and catch degradation before users do.
When measurements show drift:
