A Comparative Literature Review of Built-in AI-Powered Incident Response Agents Across AWS, Microsoft Azure and Google Cloud Platform

In today's cloud Amazon Web Services (AWS), Microsoft Azure and Google Cloud Platform (GCP) environments, traditional incident response becomes cumbersome, and vulnerable to human error. While Artificial Intelligence for IT Operations (AIOps) helped with machine learning in the area of anomaly detection, it remained advisory. The recent advances in Large Language Models (LLMs) and Agentic AI enable the construction of cloud architectures that can self-repair and function autonomously.With the recent advancements in Large Language Models (LLMs) and Agentic AI, it becomes possible to develop cloud architectures capable of self-healing and self-operationalizing. While there are many works that introduce a theoretical approach, or review a single vendor's built-in AI incident-response agent, no empirical study directly compares the built-in AI incident-response agents of the major hyperscalers in a similar workload and using the same fault injections. In AWS, Azure SRE Agent and AWS DevOps Agent, and in Azure and GCP, Azure SRE Agent and GCP Security AI, a standardized application (cloud_incident_tester) is deployed to allow for a fair comparison of the two agents. Detection Latency, Investigation Wall Clocks Time, Root Cause Analysis (RCA) accuracy, Tool execution error recovery, and Governance guardrails are what we measure. Azure SRE Agent reduces autonomous resolution time (3m 40s- 5m00s), but needs to have a binding response plan, and will incur continuous billing costs of $0.40/hr. AWS DevOps Agent provides a deep investigative capability for a multi-region infrastructure with parallel subagents (9m 30s to 15m 23s), and will identify misconfigurations to the infrastructure without relying on a governance model– it just proposes them. The difference between GCP and other autonomous agents is that GCP does not provide any individual agent for users, but only enterprise organization security features with AI. The findings provide useful reference points for technology leaders and reveal configuration issues at a system level that could affect the reliability of a cloud.