arXiv AI
The Abstention Protocol: RCA for Clos Fabrics
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arXiv Machine Learning
Aug 4
CENTILE: A Telemetry Foundation Model Evaluated by the Decisions It Drives
arXiv:2608. 01725v1 Announce Type: cross Abstract: Modern computing and networking infrastructure emits telemetry continuously, yet operators convert it into decisions with a separate predictor per task, entity, and horizon.
arXiv AI
Sep 15
Safety Signals to Verify NetOps Agents with Action-Level Granularity
arXiv:2609.14422v1 Announce Type: new Abstract: Agentic Network Operations (NetOps) are an emerging paradigm promising to enable workload-aware, self-adjustable, and reliable autonomous networks. Whi...
arXiv AI
Aug 18
Agent-Native Telemetry: Verifiable State-Delta Evidence for Autonomous Operations
arXiv:2608. 16178v1 Announce Type: cross Abstract: Operational telemetry is predominantly engineered for human reading: systems repeatedly serialize verbose prose, static keys, and redundant context across billions of log lines.
arXiv AI
Oct 7
AegisFlow: A Multi-Agent Agentic AI Framework for Autonomous Remediation and Self-Healing in Fragile Data Ecosystems
AegisFlow is a multi‑agent AI framework that automates the detection and remediation of failures in data pipelines, using a Watchdog agent for telemetry and a Repair agent that generates, tests, and deploys code patches via large language models. It employs a non‑intrusive Parallel Shadow Patching approach based on the MAPE‑K loop to validate patches in digital twin environments. Experiments across five failure scenarios show a 98.1% reduction in mean time to repair—from 170 minutes to 3.2 minutes—and a 92% patch success rate, freeing up 98% of data engineering on‑call time for innovation.
arXiv AI
Aug 3
SREGym: A Live Benchmark for AI SRE Agents with High-Fidelity Failure Scenarios
arXiv:2605. 07161v3 Announce Type: replace Abstract: AI agents are increasingly used to diagnose and mitigate failures in production systems, known as agentic Site Reliability Engineering (SRE).
arXiv Machine Learning
Jul 21
ClouDens: Operational Context-Aware Anomaly Detection for Large-scale Cloud System Monitoring
arXiv:2607. 18127v1 Announce Type: cross Abstract: With the rapid growth of cloud computing infrastructures in scale and complexity, network monitoring for Large-scale Cloud Systems (LCSs) has become increasingly challenging, requiring automated and reliable anomaly detection to maintain service availability.