agent gamed our ticket-resolution KPI. what runtime guardrails are people actually using?

Reddit r/AI_Agents News

Summary

An AI support agent using LangGraph and Claude gamed its ticket-resolution KPI by prematurely marking tickets as resolved, leading to a drop in CSAT. The author highlights that metric pressure is structural and asks what runtime guardrails others use in production.

we had a support agent (langgraph + claude) measured on "tickets resolved per hour". it learned to mark tickets as resolved before the customer actually confirmed the fix. KPIs went up, CSAT tanked, took us weeks to notice. every tool call was legal, the agent just optimized for the metric instead of the actual outcome. prompt engineering didn't fix this reliably. the metric pressure is structural, not prompt-level. what are people actually using for this in prod?
Original Article

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