Cases13 cases

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1. IL-0022 Three overlapping infrastructure bugs (context-window misrouting, TPU output corruption and an XLA top-k miscompilation) degraded Claude's responses

   Anthropic2025

   Symptoms

   From early August 2025 a number of users reported that Claude's responses had become worse. At first the reports were hard to tell apart from the normal variation in user feedback. By late August they had become frequent and persistent enough for Anthropic to open an investigation.

   Response quality degradationMisrouted requests

   Trigger

   A bug from 5 August that sent some Sonnet 4 requests to servers configured for the upcoming 1M-token context window; a misconfiguration deployed to the Claude API TPU servers on 25 August; token-selection and sampling-code changes deployed on 25 and 26 August that removed a December 2024 workaround and exposed a latent XLA:TPU compiler bug; and a routine load-balancing change on 29 August that increased the misrouted traffic.

   Root cause

   Software defect

   Component

   Model request routingTPUXLA compiler

   Contributing factor

   Overlapping failuresMonitoring blind spotLimited debug accessIgnored early signal

   Detection

   User reports of degraded responses from early August, including reports online. They were hard to tell apart from normal feedback variation, and their rising frequency and persistence by late August led Anthropic to open an investigation; internal evaluations did not detect the degradation.

   Remediation

   • Fixed the routing logic so short- and long-context requests go to the correct server pools (deployed 4 September, rolled out to all platforms by 18 September)
   • Rolled back the TPU server change that corrupted token generation (2 September)
   • Rolled back the top-k sampling change for Haiku 3.5 (4 September), Opus 3 (12 September) and Sonnet 4
   • Switched from approximate to exact top-k and standardized some additional operations on fp32 precision

   Action items

   • Add detection tests for unexpected character outputs to the deployment process
   • Develop more sensitive evaluations that reliably tell working and broken implementations apart
   • Run quality evaluations continuously on true production systems
   • Build infrastructure and tooling to debug community-sourced feedback faster without sacrificing user privacy
   • Fix the approximate top-k compiler bug together with the XLA:TPU team

   Lessons

   Benchmarks, safety evaluations and canary deployments missed degradation that users noticed, partly because the model often recovers from isolated mistakes, and Anthropic relied too heavily on noisy evaluations, with no clear way to connect rising reports to each of its recent changes. Overlapping bugs with different symptoms on different platforms looked like random degradation, and privacy controls on user interactions kept engineers from examining the interactions needed to identify or reproduce the bugs. Anthropic concluded that it needs evaluations running continuously on production and a continuous signal from users, such as the /bug command in Claude Code and the thumbs-down button in the Claude apps.

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2. IL-0120 A daily scheduled OS update restarted systemd-networkd on cloud-hosted GPU nodes, which conflicted with OpenAI's networking agent and removed all routes, cutting capacity for ChatGPT and the API

   OpenAI2025

   Symptoms

   At 11:36 PM PDT on 9 June, alerts fired across multiple OpenAI services as a large number of cloud-hosted GPU nodes lost network connectivity, and the engineering team was escalated to immediately. Losing those nodes cut the capacity available to serve ChatGPT and the API.

   High error rateGPU unavailable

   Trigger

   A routine daily scheduled update of the host operating system on the cloud-hosted GPU servers restarted systemd-networkd on the affected nodes.

   Root cause

   Breaking change in an upgrade

   Component

   systemd-networkdNode operating system image

   Contributing factor

   No staged rolloutLimited debug access

   Detection

   Alerts fired immediately across multiple services when the infrastructure began losing network connectivity at 11:36 PM PDT, and the engineering team was escalated to at once.

   Remediation

   • re-imaged the affected GPU virtual machines
   • deployed recovery automation to restart the remaining impacted nodes
   • halted the background update mechanisms on the GPU VMs

   Action items

   • Disable automatic daily updates on GPU VMs so updates run in a controlled, scheduled way
   • Update system configurations to prevent conflicts between systemd-networkd and the networking service
   • Audit VM configurations across the fleet to find and reduce similar risks
   • Improve recovery speed for critical infrastructure such as GPU VMs and clusters
   • Run regular disaster recovery drills

   Lessons

   An unattended operating system update that restarts systemd-networkd can wipe the routes another networking agent manages, and it hits every node that takes the update at about the same time. Without break-glass tooling to restore connectivity, nodes that lost their network had to be re-imaged, which stretched recovery over many hours.

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3. IL-0085 A control plane node of Magic Pages' Kubernetes cluster on Hetzner Cloud ran out of memory, the cluster's own calls then used up the Hetzner API rate limit, and all customer websites were down for about 7.5 hours

   Magic Pages2025

   Symptoms

   At around 09:33 UTC one of the three control plane nodes of the Kubernetes cluster that hosts Magic Pages went offline. Looking at the node showed that it had run out of memory.

   Service outageNode NotReadyCloud API rate limit exhausted

   Trigger

   One of the three control plane nodes ran out of memory and went offline at about 09:33 UTC; the attempts to replace it with hetzner-k3s failed while the cluster's own components were exhausting the Hetzner API rate limit.

   Root cause

   Capacity exhaustion

   Component

   Cloud controller managerPersistent volumes and stateful sets

   Contributing factor

   Unknown limitRetry stormMonitoring blind spotLimited debug accessSingle point of failureNo runbook

   Detection

   The founder found the control plane node offline at about 09:33 UTC. Monitoring alerts about unreachable customer websites started around the time the replacement attempts failed, and about half an hour of investigation identified the exhausted Hetzner API rate limit.

   Remediation

   • powered off all servers of the Kubernetes cluster to stop the API requests so the Hetzner rate limit could recover
   • waited until ratelimit-remaining had built up a buffer before starting recovery
   • restarted the control plane servers
   • started the essential Longhorn components by hand so Longhorn could heal and make storage volumes available
   • restarted the remaining websites by hand

   Action items

   • Monitor the Hetzner API call rate coming from the Kubernetes cluster nodes themselves to get early warning of high usage
   • Get a higher Hetzner API rate limit; Hetzner support was contacted
   • Investigate how to reduce the cluster's reliance on a single API, including alternatives to hetzner-k3s
   • Document and test a procedure for recovering Longhorn after a full cluster shutdown

   Lessons

   The running cluster, not only the deployment tooling, consumed the cloud provider's API quota, and in a failure it consumed it fast enough that the quota never recovered. The default limit of 3600 requests per hour was not enough for failure and recovery scenarios, and API usage from inside the cluster was not monitored. A tool that hides the provider's API errors slows diagnosis. After a full, non-graceful shutdown, Longhorn needed manual intervention to come back, which lengthened the outage.

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4. IL-0005 A new telemetry service overloaded the Kubernetes API servers of large clusters and broke DNS-based service discovery for all OpenAI services

   OpenAI2024

   Symptoms

   Alerts fired at 3:13 PM PST on 11 December 2024, and from 3:16 PM every OpenAI service was significantly degraded or completely unavailable, including ChatGPT, the API and Sora. The status page reported API calls returning errors and difficulties logging in to platform.openai.com and ChatGPT. Customer impact peaked at 3:40 PM.

   Service outageHigh error rateControl plane unresponsiveDNS resolution failures

   Trigger

   At 2:23 PM PST a change introducing a new telemetry service, which collects detailed Kubernetes control plane metrics, was merged and the deployment pipeline started. Between 2:51 PM and 3:20 PM the change was applied to all clusters.

   Root cause

   Configuration change

   Component

   Kubernetes API serverClient-side DNS resolver and cacheTelemetry agent

   Contributing factor

   Unrepresentative stagingNo staged rolloutCache-masked failureMonitoring blind spotLimited debug access

   Detection

   OpenAI's detection tools worked: alerts fired at 3:13 PM PST, a few minutes before customer impact began at 3:16 PM, and the root cause was identified at 3:16 PM.

   Remediation

   • Scaled clusters down to reduce the total Kubernetes API load
   • Blocked network access to the Kubernetes admin APIs so no new expensive requests arrived
   • Scaled up the Kubernetes API servers
   • Removed the telemetry service once enough control was back
   • Moved traffic from impacted clusters to healthy ones
   • Intervened manually in clusters where simultaneous resource downloads saturated resource limits

   Action items

   • Put every infrastructure configuration change through a phased rollout with continuous monitoring of both the service workloads and the clusters, including the Kubernetes control plane
   • Run fault injection tests in which the data plane runs without the control plane for longer, and tests that roll out bad changes on purpose to check detection and rollback
   • Build break-glass mechanisms so engineers can reach the Kubernetes API servers in any circumstances
   • Decouple the Kubernetes data plane from the control plane so that service discovery for critical workloads does not depend on the control plane
   • Speed up recovery with better caching and dynamic rate limiters for resources needed at cluster startup, and regular drills that rapidly replace an entire cluster

   Lessons

   Several systems and processes failed at once and interacted in unexpected ways: tests did not catch the change's effect on the Kubernetes control plane, DNS caching put a delay between the change and the failures, and being locked out of the control plane made remediation very slow. Because service discovery used Kubernetes DNS, the control plane was carrying load for product traffic even though the data plane was meant to be independent of it.

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5. IL-0118 A global change to Kubernetes namespace labels made the networking layer recompute metadata and overwhelmed the control planes of three of OpenAI's largest GPU clusters

   OpenAI2024

   Symptoms

   From 10:15 am PT on 25 November 2024 a large share of both API and ChatGPT traffic failed with timeouts and 503 errors.

   High error rateHigh latencyControl plane unresponsive

   Trigger

   A global change to Kubernetes namespace labels caused the networking layer to recompute metadata. The change had rolled out to the small clusters without problems.

   Root cause

   Configuration change

   Component

   Kubernetes API server

   Contributing factor

   Excessive blast radiusLimited debug access

   Remediation

   • moved workloads out of the impacted clusters by hand
   • with the cloud provider, scaled up the number of control plane nodes while scaling down the number of data plane nodes, which let the control planes recover

   Action items

   • Lock namespace changes so this incident cannot recur
   • Freeze certain types of infrastructure deploys
   • Allowlist identities to prevent a recurrence of this class of incidents
   • Improve the GPU load balancing logic so traffic is re-routed automatically and more effectively when possible
   • Improve change management for fleet-wide infrastructure changes

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