incident_management · saas · workflow
Anatomy of an AI agent incident: Gradient Labs resolves memory and latency issues in production
Gradient Labs' production AI agent experienced unexplained high memory usage followed by elevated latency, both difficult to diagnose because daytime redeployments masked the memory growth and variable traffic obscured the latency root cause.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Memory usage alert fires
Google Cloud platform alerts fired indicating abnormally high memory usage across the agent's containers.
Tools used
GoCloud RunTemporalGoogle Cloud ProfilerTemporal Cloud
Outcome
Tuning the Temporal worker cache size resolved the memory issue. The subsequent latency bottleneck was resolved in less than an hour by manually setting a minimum instance count.
What failed first
The Temporal Workflow cache was filling containers beyond their memory limit causing crashes, and Cloud Run's crash-recovery had been auto-scaling to compensate—masking the root cause. Fixing the cache inadvertently stopped the crash-driven scaling, causing the agent to scale down and introducing a latency bottleneck.
Results
Time savedless than an hour
Volume5x
Grounding & classification
Source type: technical build writeup
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ai agentconversational aifailure mode describedhuman review describedproduction runtime claimedtools describedworkflow describedsoftwareresolution time reductiontechnical build writeupcustomer supportincident managementescalation workflowmonitor detect alert