GitHub published a detailed post-incident report explaining what caused the roughly 7-hour-47-minute outage that hit GitHub.com, Actions, Pull Requests, Issues, Copilot, and authentication on August 17. Peak error rates reached around 20% for general web and API traffic and roughly 50% for downloads of archives and raw repository content. According to the report, no code change or configuration mistake triggered the incident; instead, critical infrastructure in GitHub's US Central data center failed to auto-scale processing capacity fast enough to absorb a record spike in traffic, which cascaded into a broader capacity shortage across dependent services. CTO Vladimir Fedorov acknowledged the impact directly, saying "if you were trying to ship software that day, we let you down," and disclosed that GitHub's monthly commit volume has roughly doubled, from 1.4 billion to 2.9 billion, since April. The company said it had already been working to scale infrastructure capacity by a factor of 10 over the past year but has now determined it needs roughly a 30x increase to keep pace with current and projected demand, much of it driven by AI-assisted coding workflows generating far more commits, Actions runs, and Copilot requests than traditional human-paced development. This is a notable case study for any team running infrastructure at scale, particularly platforms that sit in the critical path for other companies' CI/CD pipelines. The core lesson is that auto-scaling systems tuned for historical growth curves can fail silently until they hit a wall, especially when the growth driver, AI agents committing and running pipelines continuously rather than humans working in bursts, changes the shape of demand rather than just its volume. Platform engineering and SRE teams should read this as a prompt to re-examine their own capacity models: are your auto-scaling thresholds and load-testing scenarios still valid if a meaningful share of your traffic is now machine-generated rather than human-paced, and does your incident response plan account for cascading failures in downstream systems when a single regional data center saturates.