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title: So Agentic Systems Are Messing Up Your SLO Framework
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# So Agentic Systems Are Messing Up Your SLO Framework

**[DevOps.com](https://daily.dev/sources/devops)** · 14 min read · 1 upvotes · 0 comments

## Summary

Traditional SLOs built on deterministic contracts break down when applied to agentic AI systems, where identical inputs can produce different plausibly correct outputs and quiet failures never surface as errors. Practitioners from TestMu AI, Ciroos, FORMA, and Oracle have independently converged on a three-layer SLO model: infrastructure reliability (uptime, latency, errors), inference reliability (token latency, fallback frequency, response variance), and behavioral correctness (semantic drift, output variance, confidence thresholds). Key new metrics include step count as a confusion signal, pass@k distributional correctness, intent alignment scoring, and probabilistic tracing that treats decision points—not requests—as the primary unit of observation. Separate error budgets per layer are essential for proper incident attribution and team routing. The field still lacks shared standards for acceptable nondeterminism, and teams must currently define behavioral thresholds empirically from production data.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://devops.com/so-agentic-systems-are-messing-up-your-slo-framework>

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---

Tags: [#observability](https://daily.dev/tags/observability), [#platform-engineering](https://daily.dev/tags/platform-engineering), [#agentic-ai](https://daily.dev/tags/agentic-ai)

[View this post on daily.dev](https://daily.dev/posts/so-agentic-systems-are-messing-up-your-slo-framework-ueygqji6y)

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