AGENTIC SRE TEAMS: HUMAN-AGENT COLLABORATION - A NEW OPERATIONAL MODEL FOR AUTONOMOUS INCIDENT RESPONSE

Abstract

Site Reliability Engineering teams face escalating operational complexity as distributed systems grow in scale and sophistication, creating incident response challenges that exceed human cognitive and temporal limitations. This research develops and evaluates a novel operational model where autonomous AI agents collaborate with human SREs as integrated team members rather than passive automation tools. The agentic SRE framework enables AI agents to autonomously detect incidents, perform initial triage, execute diagnostic workflows, implement approved remediation actions, and escalate complex issues to human operators with comprehensive context. Implementation across three production environments supporting 2.8 million users demonstrated that human-agent collaborative teams reduced mean time to detection from 12 minutes to 45 seconds and mean time to resolution from 43 minutes to 8.7 minutes—an 80% improvement. AI agents autonomously resolved 67% of incidents without human intervention while successfully escalating the remaining 33% with diagnostic context that accelerated human troubleshooting by 56%. The collaborative model improved SRE team capacity by 340% measured in incidents handled per engineer, enabling organizations to maintain larger-scale systems without proportional headcount increases. Human operator satisfaction improved 31% through elimination of repetitive toil and focus on challenging problems requiring creativity. This research contributes a practical framework transforming SRE operations from human-centric reactive troubleshooting to collaborative human-agent proactive management.