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Routing and reliabilityBrief

LLM incident response: diagnose failures across providers

AI incidents cross application, gateway, provider, model, prompt, data, and tools. Triage quickly by separating availability, latency, quality, and spend.

By INFRO Editorial

Published

Updated

1 min read

An AI operations team view isolating a red failure signal inside a network of healthy routes

In short

LLM incident response should first classify the symptom as availability, latency, quality, data, tool, or spend, then correlate it with model, route, attempt, prompt version, and workload. Recovery policy and complete request traces reduce time spent guessing across provider dashboards.

On this page
  1. Triage by symptom
  2. Stabilize before perfect diagnosis
  3. Collect the evidence chain
  4. Close the loop

Triage by symptom

Check whether requests fail, arrive slowly, return invalid structure, produce worse answers, call tools incorrectly, expose data, or cost more. These categories have different owners and mitigations even when they start at the same model release.

Compare affected and unaffected traffic by model, provider route, region, project, prompt version, and feature.

Stabilize before perfect diagnosis

Remove a degraded route, reduce traffic, disable an unsafe fallback, roll back a prompt, lower concurrency, pause a batch job, or restrict a premium model. Choose the smallest reversible action that protects users and data.

Do not let automatic failover hide a growing incident. Rising fallback rate is a signal even when final errors stay low.

Collect the evidence chain

For example requests, capture request ID, timestamps, model, route, attempts, errors, first-token and total latency, usage, cost, prompt or configuration version, tools, and final product outcome. Keep sensitive payload access controlled.

Provider status pages are context, not proof. Your route-level evidence shows whether your traffic is affected.

Close the loop

Write the trigger, impact, detection gap, mitigation, root cause, and concrete prevention. Add the failure to evaluations, alerts, runbooks, or policy. If the response depended on one person remembering a dashboard, automate the next detection.

Review whether fallback preserved quality and whether recovery attempts changed the bill.

From the team that builds it

Where this shows up in INFRO

INFRO is a control plane in front of the provider accounts you already hold. The capabilities below are the parts of it this article touches, each with the documentation that defines its behaviour.

INFRO accounts are self-serve. Provider usage is billed to you by your providers; INFRO charges a software subscription and takes 0% of model spend. See plans.

About this article

Last checked against its sources on . The next review is due by . No separate named reviewer is recorded for this article.

How articles are written, sourced and corrected is set out in the editorial standards.

Revision history

  1. Removed the promotional template sections and the generic FAQ, moved product context into the product section, and corrected product wording to the current BYOK model.

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