<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Slo on CuraDevOps</title><link>https://curadevops.metacog.co.kr/tags/slo/</link><description>Recent content in Slo on CuraDevOps</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Fri, 25 Sep 2026 15:41:44 +0000</lastBuildDate><atom:link href="https://curadevops.metacog.co.kr/tags/slo/index.xml" rel="self" type="application/rss+xml"/><item><title>Applying SLO error budgets to AI agent hallucination rates</title><link>https://curadevops.metacog.co.kr/insights/2026-09-25-what-if-your-agent-s-hallucinations-had-a-budget-how-to-star/</link><pubDate>Fri, 25 Sep 2026 15:41:44 +0000</pubDate><guid>https://curadevops.metacog.co.kr/insights/2026-09-25-what-if-your-agent-s-hallucinations-had-a-budget-how-to-star/</guid><description>&lt;ul>
&lt;li>&lt;strong>Platform/SRE — Learn:&lt;/strong> Grafana&amp;rsquo;s approach maps traditional SLO/error-budget mechanics onto agent evaluation scores, giving SREs a familiar reliability framework for operating AI systems; no production change needed today, but worth reviewing before instrumenting any agent workloads.&lt;/li>
&lt;li>&lt;strong>CI/CD — Skip&lt;/strong>&lt;/li>
&lt;li>&lt;strong>Leader — Learn:&lt;/strong> Framing agent quality as an error budget (measurable, target-driven) is a useful mental model for leaders deciding how to set reliability expectations and observability standards for AI agent initiatives.&lt;/li>
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