Agentic AI in Major Incident Management: Challenges & Vision

The article, authored by Cutover's CEO, delves into the evolving landscape of major incident management (MIM) and positions agentic AI as a pivotal technology for overcoming persistent challenges in enterprise IT. It begins by painting a familiar picture of a P1 incident at 2 AM: a chaotic bridge call, frantic Slack activity, and a lack of clear direction, often leading to prolonged outages. This scenario, the article contends, is a common reality, with a 2025 study indicating that 65% of enterprises experienced a major incident in the past year. The core argument is that simply adding more engineers to a bridge call does not shorten resolution times; rather, the absence of structured execution is the primary impediment. Traditional MIM is characterized by several inefficiencies: unclear ownership, reliance on tribal knowledge, and inadequate audit trails, making post-incident reviews difficult and hindering continuous learning. Each incident often feels like "Groundhog Day," with no structured learning to prevent recurrence. The article emphasizes that the average major incident can drag on for over three hours, leading to significant revenue loss and compliance exposure, particularly in regulated industries. The author differentiates agentic AI from generic AI models or chatbots. While chatbots observe and suggest, agentic AI is built to execute. It operates within structured runbooks, progressing real tasks as dependencies clear, with human-in-the-loop approval at critical decision points. This distinction is crucial because generic AI, trained on public data, lacks situational awareness of an enterprise's unique architecture, legacy code, and internal terminology. For agentic AI to be effective in MIM, the article outlines several key insights. Firstly, there is no "one-size-fits-all" data. Every enterprise's IT estate and incident processes are unique, requiring enterprise-specific training for AI models. Detailed incident graphs, capturing the sequence of human and automated steps, decisions, and communications, are more valuable than mere logs for providing situational awareness to the AI. Secondly, privacy and compliance are paramount, meaning enterprises prefer private models over sharing sensitive incident data. The vision for agentic AI in MIM is not to replace human incident managers but to empower them. AI agents can handle the "grunt work" of sifting through data, diagnosing issues, surfacing insights, proposing next steps, and even executing fixes under human supervision. This "assistive mode" allows the AI to build reliability and track record, eventually gaining more autonomy with proper guardrails and fallbacks. The ultimate goal is to achieve fewer outages, faster recovery, and enable Site Reliability Engineering (SRE) teams to shift their focus from reactive firefighting to strategic improvements, ushering in an era of enhanced resilience.