Causely: A Causal Intelligence Layer for Enterprise AI A Benchmark Study on SRE and Reliability Workflows
The paper introduces Causely, a causal intelligence layer designed to enhance AI agents in Site Reliability Engineering (SRE) workflows. It provides a structured representation of environment topology and causal relationships, improving the efficiency of AI diagnostics. Benchmark studies show significant reductions in diagnosis time and costs when using Causely compared to traditional methods.
- ▪Causely transforms raw telemetry into a live, queryable model for AI agents.
- ▪The benchmark study demonstrated a 63% reduction in mean time-to-diagnosis during active incidents.
- ▪Root-cause-diagnosis accuracy improved from 75% to 100% with Causely.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Artificial Intelligence arXiv:2605.18327 (cs) [Submitted on 18 May 2026] Title:Causely: A Causal Intelligence Layer for Enterprise AI A Benchmark Study on SRE and Reliability Workflows Authors:Dhairya Dalal, Endre Sara, Ben Yemini, Christine Miller, Shmuel Kliger View a PDF of the paper titled Causely: A Causal Intelligence Layer for Enterprise AI A Benchmark Study on SRE and Reliability Workflows, by Dhairya Dalal and 4 other authors View PDF HTML (experimental) Abstract:AI agents deployed into SRE workflows currently derive their understanding of environment state from raw observability telemetry at query time, paying a semantic-interpretation tax in tokens, latency, and inferential reliability.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
Discussion
0 commentsMore from arXiv cs.AI
