Circle K用Amazon Bedrock智能体缩短事故分诊
Circle K基于Amazon Bedrock构建了智能体系统,可自主调查DevOps事故,并在数分钟内将可能根因的结构化摘要发送至Slack。据AWS Machine Learning Blog和Circle K内部事故响应指标,平均分诊时间从45至60分钟降至不足15分钟,降幅为67%至75%。该系统最初使用Amazon Bedrock Flows和Knowledge Bases,之后迁移至Amazon Bedrock AgentCore Runtime上的单一智能体。
这一企业案例显示,Amazon Bedrock智能体能够自动执行跨工具事故调查并缩短工程师收集信息的时间,但相关成效数据来自Circle K内部指标。
This post was co-written with Marek Klatowicz, Cloud DevOps Engineer at Circle K.
Many DevOps teams know the incident triage pattern: An alert fires, and before anyone can fix the problem, an engineer spends 45–60 minutes figuring out what happened. They pull logs, check service health, and correlate symptoms across a handful of tools. All of this happens before remediation can begin.
At Circle K, this challenge scaled with the business. The company operates more than 17,200 convenience and fuel retail stores across 27 countries. Behind the storefronts, a cloud-native infrastructure supports point-of-sale systems, loyalty applications, and electric vehicle (EV) charging applications running on AWS. As the company expanded its digital operations, the triage overhead grew with it. More services meant more alerts, and each one required the same manual investigation before remediation could begin.
To address it, Circle K built an automated triage system using Amazon Bedrock, a fully managed service that makes foundation models (FMs) available through a single API. For model availability by AWS Region, refer to Supported models by AWS Region in Amazon Bedrock. The system uses an agentic AI architecture to investigate each incident autonomously and deliver a structured summary to Slack within minutes of an alert firing. Instead of raw alerts, on-call engineers now receive actionable context, so they can act immediately rather than spending an hour figuring out what happened.
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