The original title is: "Hands-on: Running an AI agent on cloud ops for 7 days — wins, failures, and guardrails"

Short summary

An engineer gave an AI agent read access to a monitoring stack and limited write access to a Kubernetes cluster for one week. The agent excelled at triage speed and context gathering, cutting MTTR from ~45 to ~20 minutes on routine incidents, and caught a slow memory leak before it paged. However, it was confidently wrong on 3 of 11 alerts, once proposing to scale a broken service 10x, which would have worsened the outage without manual guardrails. The takeaway: let AI handle the boring 80% of context gathering, but keep risky production changes behind human approval.

• •AI agent cut incident triage time significantly by pre-gathering logs, metrics, and deploy context before human review
• •Agent was confidently wrong 3/11 times, including a dangerous proposal to 10x-scale a misconfigured service
• •Manual approval guardrails prevented a real incident; token cost was ~$30/week but needs budget caps

Generated with AI, which can make mistakes.