Been sitting on this for a bit after watching an AI feature at my last job basically die a slow death post-launch, and I think the model-failure explanation is usually a red herring tbh. Concrete version of what I mean. We had an agent doing first-pass triage on inbound support tickets, routing + drafting a suggested reply for a human to approve. Launched, looked great for like 6 weeks. Engineering was watching latency (fine, consistently under 2s) and error rate (also fine, sub 1%). Product was watching ticket resolution time, which actually improved initially. Meanwhile the support team itself started quietly noticing the suggested replies were getting weirdly generic for a specific category of tickets, nothing crashing, nothing erroring, just worse. They mentioned it in a slack channel a couple times. Nobody connected it to anything bc it wasnt anyone's job to connect it, support flagged quality, eng was looking at uptime, product was looking at a downstream metric that hadnt actually moved yet bc the degradation was gradual. By the time it showed up as an actual problem (resolution time metric finally dipped, maybe 2 months in) everyone's first assumption was "the model must have changed" or "we need a better prompt." Root cause when we actually dug in was a data source the agent pulled context from had silently started returning stale info after an unrelated pipeline change. Not a model problem at all. A "three teams had three different partial views of the same system and none of them overlapped" problem. Seen versions of this with teams running LangSmith, Langfuse, even fully custom setups someone built in-house. The specific tool wasnt really the variable. What was missing every time was something dumber than tooling, just a shared place where the trace, the quality complaint, and the downstream metric could actually sit next to each other and get looked at by someone who could act on all three at once. Could be pattern matching on too small a sample, genuinely not sure. But curious if this tracks for anyone else. What actually killed your AI feature after launch, was it actually the model, or was it more of a "nobody owned the full picture" thing dressed up as a model problem after the fact
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