arXiv:2607. 17525v1 Announce Type: new Abstract: Multi-provider LLM gateways reverse proxies that route, load-balance, and rate-limit requests across foundation-model APIs have become critical production infrastructure.
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:2606. 31567v1 Announce Type: cross Abstract: Flaw reporting for deployed AI systems is fundamental to identifying system failures and improving AI safety.
arXiv:2609. 11030v1 Announce Type: new Abstract: AI agents increasingly act through tools and delegated authority, but general incident repositories rarely capture the mechanisms needed to compare public failures with agent-security evaluations.
arXiv:2608. 02786v1 Announce Type: new Abstract: AI systems can fail silently.
arXiv:2610.15459v1 Announce Type: cross Abstract: LLM-based multi-agent systems (LLM-MAS) use specialized agents that exchange messages to accomplish complex tasks. Their reliance on networked protoc...
arXiv:2602.02475v2 Announce Type: replace Abstract: AI agents often fail in ways that are difficult to localize because executions are probabilistic, long-horizon, multi-agent, and mediated by noisy...
The paper outlines Meta’s deployment time health check infrastructure, called Service Health Checker, which mediates the trade‑off between rapid release velocity and system reliability across thousands of heterogeneous services. It explains how check authors compose templated metric queries, thresholds, and workflow predicates, and how the system integrates with tiered and phased rollouts to trigger automatic rollback on regressions. The authors also discuss operational challenges that arose at scale—such as noise, alert fatigue, drift, and uncovered regressions—and the measurement, tooling, and default improvements they implemented to address these issues, concluding with lessons learned and future directions including AI‑assisted tuning.
arXiv:2606. 05339v1 Announce Type: cross Abstract: MCP (Model Context Protocol) enables LLMs (Large Language Models) to interact with external tools and data sources via a standardized protocol.
AI agents increasingly act through tools and delegated authority, but general incident repositories rarely capture the mechanisms needed to compare public failures with agent-security evaluations. We...
Incident‑Arena is a new benchmark for AI coding agents focused on production incident response, featuring 20 tasks derived from real‑world open‑source software. Each task deploys a production application on an ephemerally created Kubernetes cluster, injects faults at various layers, and applies a sustained load profile. The benchmark introduces functional verifiers that maintain system‑level metrics while ensuring safe repairs, and shows that current frontier models achieve below 64.3% across the tasks, highlighting challenges in diagnosis, repair, and regression safety.
arXiv:2610.16067v1 Announce Type: cross Abstract: Agent evaluation harnesses are software systems that run test suites, generate inputs, and decide outcomes with test oracles. This paper audits such...