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Incident Response in the Age of AI (Incident Fest)

Sam Salter

Tags:<br>AI & Automation

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Incident Management

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https://uptimelabs.io/articles/incident-response-ai

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Incident Fest is our fun, free virtual festival. More importantly, it’s a place for incident responders to share their stories and learn from others.<br>This year, we were delighted to welcome a variety of exceptionally talented speakers to the stage to discuss the evolving (and not always harmonious) AI/incident response relationship. Note that these are a few highlights, so please go and watch the full talk recordings after!<br>The Theme

Where AI Offers Wins<br>All three speakers were upfront that AI is already pulling real weight in incident response:<br>Stu Rimell (Uptime Labs) noted that LLM tooling "is starting to show promise in addressing the low-hanging fruit of incident response" i.e. auto-summarising chat threads, intervention suggestions, remedial PRs.<br>On code itself: "AI is a tremendous tool for understanding code"<br>On freeing up attention: "the idea of being able to offload some or most of that cognitive load is legitimately exciting", letting responders focus on the genuinely hard & novel parts of an incident instead of the rote ones.<br>J. Paul Reed (Chime) cited research showing an upside when an AI's diagnostic suggestions were correct - the humans using it "performed 53 to 67% better than when they worked without AI assistance".<br>Sylvain Kalache (Rootly) pointed to Meta's agentic mutation-testing tool (thousands of synthetic bugs generated, 73% accepted by engineers as valid tests) calling it "a massive win that would have been impossible to do manually or extremely resource-intensive"<br>His summary: "AI-assisted coding is not going anywhere. It's here to stay" - the goal isn't resistance; it's building the muscle to use it well.<br>In other words, AI offers a variety of exciting, innovative ways to make engineers’ lives easier. The question then becomes ‘how do we enable AI safely in the short and long term?’ - which is the question the festival aims to unpack carefully.<br>The Leftover Principle (Stu Rimell, Uptime Labs)<br>Stu opened with a story: he’d just landed in Seattle for SREcon and his rideshare app was convinced he was standing on the street outside the terminal when he was actually three floors up in the parking garage. GPS is a solved problem - until the moment it isn’t, and you’re back to reading signs and asking strangers for directions.

(editors note: Stu was thankfully able to eventually leave the car park and get captured in this instantly iconic photo in Seattle)Stu’s story, he said, is exactly what it feels like every time automation reaches the edge of what it can do.<br>The Leftover Principle describes the tasks left over once automation has done all it can, or was designed to do.<br>Leftover tasks tend to be either too trivial to bother automating or too rare, complex and novel to automate at all.<br>Incidents fall squarely into that second (gnarlier!) category.<br>Historical grounding<br>The concept traces back to Alphonse Chapanis: the ‘godfather of human factors,’ who redesigned the B-52 cockpit after pilots kept retracting the landing gear instead of the flaps. Then, it passed to David Woods and Erik Hollnagel, who pushed back on ‘automate everything; thinking. The canonical reference is Lisanne Bainbridge’s 1983 paper Ironies of Automation - required reading, Stu notes. Its key paradox: the more you automate, the more important the human role becomes, not less.<br>At London’s OOPS community meetup, Stu heard two approaches emerging: auto-diagnosis and copilot mode. LLM tooling shows promise on the low-hanging fruit; complex scenarios remain human territory.

The Four Dragons<br>Harder leftovers : what’s left is rarer and more novel by definition - that’s why it wasn’t automated already<br>Skill atrophy : less practice erodes skills; Bainbridge warned systems would end up ‘riding on skills which later generations of operators cannot be expected to have’<br>Situational context loss : arriving only at the leftover point is ‘like coming into an argument halfway through’<br>Accountability gap : humans remain accountable for incidents even as their expertise to actually exercise that accountability erodes. The risk is ending up in a job that's "very boring but very responsible", with no real opportunity to build or maintain the incident response skills that responsibility demands.<br>Stu backed this with numbers: GitHub’s weekly commits jumped from 19 million to 275 million, and the 2026 Faros AI Engineering Report found incidents per PR up almost 250%.<br>“The dream of being able...