The Efficiency and Risk of AI Incident Response
AI incident response is automating work that once required on-call engineers. The article says these tools can inspect alerts, form hypotheses, query telemetry, correlate recent deployments, and even apply fixes themselves. For routine events such as an overnight capacity issue, that can reduce interruptions and may lower the average time to recovery for many incidents.
The central concern is not automation itself, but the growing distance between people and the systems they operate. Routine incidents are opportunities to build intuition: engineers learn how a system degrades, which signals matter, and what side effects a fix may create. When models handle those experiences for a long period, engineers may get far less practice investigating real failures themselves.
From Routine Failures to Complex Incidents
This creates an automation paradox: the better a tool handles normal conditions, the more likely humans are to take over only when a situation is ambiguous, unfamiliar, and severe. Citing researcher Lisanne Bainbridge’s work on the “ironies of automation,” the article argues that automation reduces opportunities to practice routine work without removing responsibility for novel problems. As a result, complex-incident resolution may not improve alongside routine recovery and could take longer when responders lack experience.
The article points to aviation as a useful comparison. Flight automation handles much routine operation, yet pilots remain responsible for rare events such as engine failures, unreliable instruments, and stalls. Because these situations are seldom encountered in real flights, airlines use recurring simulator training to preserve emergency skills. Software incidents usually do not threaten lives directly, but quickly interpreting incomplete information, making tradeoffs, and coordinating a team also require repeated practice.
How Incident Simulators Restore Practice
The article describes a software-focused approach: engineers take the incident commander role during a simulated e-commerce outage, use observability tools to investigate, and communicate with LLM-powered stakeholders in Slack. This tests more than technical diagnosis. Participants must work with incomplete evidence, keep the response organized, communicate with leadership and customer support, and make progress under pressure. The article says Rootly and Uptime Labs have partnered around this model.
AI can also act as a coach by showing responders which steps an agent took, which signals it examined, and what evidence supported its diagnosis. Yet explanation is not a substitute for operating experience. Watching an intelligent agent resolve an incident may clarify a process, but durable skill requires engineers to face failures themselves, bear the consequences of decisions, and repeatedly refine their approach in a simulation.
Making Skill Maintenance Part of On-Call Readiness
For teams, the practical answer is not to abandon AI but to pair automation with deliberate practice. On-call readiness can include recurring incident simulations, tabletop exercises, and carefully designed chaos engineering so engineers continue to encounter unfamiliar failure modes. Training should cover incident command, communication, escalation, and evaluation of evidence, rather than only memorizing runbooks. AI can generate stakeholders, provide clues, and support reviews, while people still make the critical decisions.
The article leaves an important question for teams to test: when AI makes routine incidents faster to resolve, have they created enough structure to prevent “comprehension debt” from accumulating? The answer depends on whether simulation is treated as part of production reliability, with difficulty and frequency adjusted through feedback. Automation should not be judged only by how many alerts it removes today. Teams must also remain able to understand, control, and restore their systems when automated help is unavailable.