Operations | Monitoring | ITSM | DevOps | Cloud

AI-related outages just moved from a fringe worry to a mainline reliability problem, and the on-call rotation is where that shift lands first. A new StackGen analysis of nearly 178,000 public status-page records found that incidents disclosed by AI model and AI application companies now account for more than one in ten reported outages, a sixfold jump from 1.7 percent in 2023 to 10.7 percent so far in 2026.

The story of 2026 engineering has a plot twist that on-call teams saw coming. AI coding assistants now write a large share of production code, pull requests per developer are up, and delivery velocity looks great on the dashboard. Then the pager goes off.

On August 6, 2026, GitHub opened an incident for degraded Actions performance at 15:22 UTC. Within about twenty minutes, Actions availability was listed as degraded, workflow runs were failing to start or failing partway through, and the Actions REST API was returning errors. Pages was pulled into the same incident shortly afterwards. The status page marked Actions and Pages as mitigated at 00:05 UTC on August 7, and closed the incident at 02:04 UTC.

On the morning of August 5, 2026, a major AI provider went dark for roughly seven and a half hours, and thousands of engineering teams learned in real time what an AI provider outage actually costs them. Anthropic's Claude models returned elevated error rates and failed API requests starting around 3:00 AM Eastern, and applications that quietly route user traffic through a large language model suddenly had no model to route to. Chatbots stopped answering. Summarization pipelines stalled.

This blog post is part of PagerDuty’s ongoing series on how we’re helping customers navigate their journey towards autonomous operations. Read on to learn about how Event Enrichment builds towards this vision. Every on-call engineer knows the drill. An alert fires. It tells you something is wrong, but not what it means. Is this asset in maintenance? Which team owns it? Is it customer-facing?

This blog post is part of PagerDuty’s ongoing series on how we’re helping customers navigate their journey towards autonomous operations. Read on to learn about how recent SRE Agent Enhancements build towards this vision. During an incident, everything is competing for attention at once. Responders lose time swiveling between tools, insights gathered by AI stay siloed instead of feeding into the next decision, and the pressure to move fast means learnings rarely stick.