{"data":{"id":12,"backendId":"cf9af595-e7da-488b-8e9c-1471fb698b45","title":"AOI: Turning Failed Trajectories into Training Signals for Autonomous Cloud Diagnosis","summary":"arXiv:2603.03378v1 Announce Type: new Abstract: Large language model (LLM) agents offer a promising data-driven approach to automating Site Reliability Engineering (SRE), yet their enterprise deployment is constrained by three challenges: restricted access to proprietary data, unsafe action execution under permission-governed environments, and the inability of closed systems to improve from failures. We present AOI (Autonomous Operations Intelligence), a trainable multi-agent framework formulati","analysis":"This research addresses critical enterprise barriers for AI agents: security, data privacy, and learning from failure using state-of-the-art RL techniques like GRPO.","category":"technology","strategicTrack":"ai_agents","capitalRelevance":{"social":2,"cultural":4,"economic":8,"symbolic":5,"technological":10,"informational":9,"temporal":7,"psychological":3,"physical":1},"tags":["SRE","LLM Agents","GRPO","Cloud Diagnosis","AIOps","Trajectory Learning"],"qualityScore":10,"valueScore":9,"interestScore":8,"potentialScore":9,"uniquenessScore":9,"sourceCount":1,"confidence":5,"detectedAt":"2026-03-05T18:09:05.125Z","createdAt":"2026-03-05 18:10:45"}}