INC177726129483822

P2resolvedai_assisted

search-api request queue depth climbing under traffic spike

Pipeline Status

Context Assembly

Diagnose Agent

Severity / Routing Agent

Scenario Match

Remediate Agent

Confidence Gate

Gate Decisionauto executed

Execute Action

Verify Agent

Resolution

Assembled Context — bounded to last 30 minutes

Rolling logs

[search-api] INFO requests/sec 4200 (baseline 1100)

[search-api] WARN queue depth 3800, p99 latency 2100ms

[search-api] INFO error rate stable at 0.2%

[autoscaler] current instances 6/6 (max reached)

Recent commits

(none in lookback window)

Similar past incidents

INC177882654715913 · application/P2 · -18d ago — search-api request queue depth climbing under traffic spike

INC17821165852576 · application/P2 · -56d ago — search-api request queue depth climbing under traffic spike

INC178038577873729 · application/P2 · -36d ago — search-api request queue depth climbing under traffic spike

Diagnosis Agent

ran in parallel

Autoscaler reached its configured maximum of 6 instances, preventing further scaling of the search‑api service during a traffic spike, which caused the request queue to grow to ~3800 and p99 latency to rise to ~2100 ms.

[search-api] INFO requests/sec 4200 (baseline 1100)
[search-api] WARN queue depth 3800, p99 latency 2100ms
[search-api] INFO error rate stable at 0.2%
[autoscaler] current instances 6/6 (max reached)

Assessed: P2 / application

Self-reported confidence: 90% (audit-only — not used by the gate)

openai/gpt-oss-120b · 2059ms

Severity / Routing Agent

ran in parallel

P2 / application

Routed to: search-api-team

The service is experiencing significant degradation with high request latency and maxed out capacity due to a traffic spike. Since the autoscaler hit its maximum instance count, the application is unable to scale further to handle the load, impacting user experience. This matches previous incidents categorized as application-level scaling issues.

Self-reported confidence: 95% (audit-only — not used by the gate)

gemini-flash-lite-latest · 1039ms

Scenario Matcher

deterministic, pre-LLM

Traffic spike exceeding capacity

Sustained traffic above provisioned capacity with healthy error rates is resolved by scaling out instances rather than restarting or rolling back.

Match score: 80% · Recommended action: scale_up

Matched tags: traffic spike, queue depth

Remediate Agent

grounded by runbook match, pre-gate

Proposed action: scale_up

team: search-api-team · reason: Autoscaler limit reached, queue depth ~3800 causing latency · message: Scaling up due to autoscaler max reached during traffic spike · serviceName: search-api · justification: Add capacity to handle sustained high traffic · targetInstances: 8

The diagnosis indicates the autoscaler hit its maximum of 6 instances, leading to a large request queue and high latency. The runbook for traffic spikes recommends scaling out, and no contradiction exists. Therefore we propose scaling up the search‑api service to 8 instances to provide additional capacity.

openai/gpt-oss-120b · 1590ms

Confidence Gate

auto_executed

Computed from 4 measurable signals — not the model's self-reported confidence.

Severity match rate100%

Runbook coverage80%

Data completeness67%

Recency100%

Computed confidence (v1)88%

Auto-execute threshold: 75%

Proposed action: scale_up

Verify Agent

runs after execution

Resolved · residual risk: low

Follow-up: none

The service successfully scaled up to 8 instances, which directly addresses the capacity bottleneck identified. Monitoring shows the request queue depth is normalizing, but a low residual risk remains if traffic continues to increase significantly, requiring further investigation into autoscaler policy limits.

Self-reported confidence: 95% (audit-only — not used by the gate)

gemini-flash-lite-latest · 870ms