AIDR-Cloud: An Agentic AI—Driven Incident Response Framework for Cloud Failures

Modern cloud-native systems comprise hundreds of microservices, multi-region deployments, and streaming data pipelines. This complexity has dramatically increased the frequency, impact, and opacity of operational failures. Traditional incident response, which is built around manual troubleshooting, static rules, and prewritten runbooks, struggles to meet today's latency and reliability requirements. This paper presents AIDR-Cloud, an agentic AI-driven incident detection and response framework that combines real-time telemetry, a domain-specific cloud knowledge graph, and multi-agent large language model (LLM) reasoning to autonomously diagnose [11] and remediate failures in cloud environments. AIDR-Cloud introduces a three-layer architecture: a Perception Layer that continuously ingests metrics, logs and traces; a Cognition Layer composed of specialized LLM agents that perform hypothesis generation, validation and remediation planning over a knowledge graph of cloud resources and dependencies; and an Action Layer that executes guarded remediation operations via Kubernetes and cloud-provider APIs under a safety and governance framework. We evaluate AIDR-Cloud along three complementary axes. First, in controlled fault-injection experiments on multi-region Kubernetes and Kafka workloads, we compare AIDR-Cloud against experienced human operators using standard observability and runbook tooling. Second, we analyze the cost and latency overheads of AIDR-Cloud by decomposing end-to-end response time into perception, cognition and action components, and by estimating LLM token usage, control-plane calls and infrastructure cost as a function of incident rate. Third, we report outcomes from a multi-week deployment of AIDR-Cloud on real production incidents, including changes in MTTR and escalation behavior, as well as qualitative improvements in postmortem quality and runbook evolution. Across both synthetic and real incidents, AIDR-Cloud reduces MTTR relative to the human baseline, achieves faster root-cause analysis, reduces the rate of human escalation, and selects correct remediation actions in a large fraction of cases, while adding only a small amount of reasoning latency and no unsafe actions due to strict guardrails. These results show that agentic AI can act as a statistically well-characterized and cost-conscious SRE co-pilot, significantly improving reliability and operational efficiency in complex cloud systems.