Cases3 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-0123 A response buffer allocated anew in a loop made OpenAI's routing layer nodes hit their memory limits and fail readiness checks on a morning with more completions than any day before, causing 502 and 503 errors across all models

   OpenAI2023

   Symptoms

   Between 5:42 AM and 7:16 AM PT on 8 November 2023 a large share of requests to OpenAI failed with 502 or 503 errors. Every model and API endpoint saw significant failures.

   High error rateService outageProbe failures

   Trigger

   A morning with dramatically more completions than any previous day pushed the routing layer nodes, which already had a chronic memory problem, past their memory limits.

   Root cause

   Software defect

   Component

   Model request routingContainer memory limit

   Contributing factor

   No alert on limitNo headroom

   Remediation

   • limited incoming traffic
   • redeployed the routing service en masse
   • ramped traffic back up slowly

   Action items

   • Pre-allocate the responseBuffer and reuse it instead of allocating a new one in a loop (3X improvement in memory and CPU usage)
   • Adjust the configured memory limits to an appropriate level so the service has significant headroom
   • Add rate limit controls to shed traffic more gracefully
   • Increase the service's capacity
   • Change alerting to catch the underlying memory behaviour before it escalates into service issues
   • Configure autoscaling for the service, which was not possible before because added capacity could hurt an upstream service

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3. IL-0132 A faulty resource cleanup process marked Vertex AI prediction endpoints as deleted worldwide, undeploying customers' models and failing predictions for 4 hours 20 minutes

   Google Cloud2022

   Symptoms

   From 10:10 US/Pacific on 2 June 2022, Vertex AI Online Prediction returned errors for 30% up to 100% of requests per region, depending on how each customer used the service.

   High error rateResources deletedService outage

   Trigger

   A faulty resource cleanup process marked Vertex Prediction Endpoints as deleted, globally.

   Root cause

   Software defect

   Component

   Model request routing

   Contributing factor

   Automation amplificationExcessive blast radius

   Detection

   The first public status update was posted at 13:58 US/Pacific and gave the start as 10:20 US/Pacific; the later mini incident report gives 10:10.

   Remediation

   • restored the Vertex Prediction Endpoints that had been marked as deleted

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