Because "it looked great in testing" is not a quality strategy for production AI.
Every team that ships an AI agent faces the same quiet problem. The agent works well at launch — you tested it, your team reviewed it, quality looked solid. Then, two weeks later, someone mentions that the responses feel "off." A customer complains. CSAT ticks down. You go looking for what changed, and there's no clear answer.
The problem isn't a single event. It's drift — slow, gradual, silent degradation across six different quality dimensions simultaneously. Your LLM provider pushed a silent model update. Your RAG index got stale. A prompt template was tweaked and the new few-shot examples don't represent your actual edge cases. None of these events look catastrophic in isolation. Together, they compound.
Walter Shewhart invented control charts at Bell Labs in 1924 to solve exactly this problem in manufacturing: how do you know when a production process is drifting before defects start reaching customers? His answer was statistical — establish a baseline, set control limits at ±3σ, and watch for patterns that signal a process going out of control before any limit is breached.
For 100 years, this has been the standard in automotive, aerospace, pharmaceuticals, and semiconductors. The method works because it uses the natural variation of the process itself as the signal. It doesn't require knowing what caused the drift — it just tells you when the statistical signature of normal variation has been broken.
AI agent outputs are a process. Hallucination rate, tone score, accuracy, response length — these vary from response to response, but they vary within a stable statistical envelope when the agent is healthy. When that envelope shifts, SPC fires. Long before users notice anything.
Agent SPC applies the same mathematical rigour to LLM output quality that manufacturers have used for critical processes since 1924. The difference is that instead of widget dimensions, we're measuring accuracy, tone, and hallucination rates.
Agent SPC is early access software for teams running AI agents in production. We're focused on making the baseline → detect → alert loop as frictionless as possible. Connect your agent via SDK or REST. Let Agent SPC establish your control limits. Get notified when statistical rules fire — before users do.
We're building in public. If you're running AI agents in production and dealing with quality drift, we'd love to hear from you.
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