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Use the first 48 hours after an AI failure as a decision window, not a race to rewrite prompts. First prove the failure is real, then identify whether the test, agent configuration, or underlying platform owns it. Assess harm in context, contain any ongoing impact, assign an incident commander, and record the evidence. Only then choose a fix and verify it across varied conditions. This order prevents a bad grader from triggering an unnecessary model change, and it keeps a local configuration mistake from being treated as a platform outage.
What should happen in the first 48 hours after an AI failure?
The goal is a defensible decision: what failed, who can change it, how serious it is, and what must be contained immediately. A failed answer is a symptom, not yet a root cause. Treat the incident as an investigation with explicit checkpoints rather than as an invitation to make an untested prompt edit.
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Hour 0–1: establish whether the failure is genuine
Freeze the evidence before changing prompts, models, tools, or evaluation code. Preserve the exact input, observed output, expected output, grader result, timestamps, and versions. Check whether the report can be reproduced and whether the same behavior appears in production, a staging run, or only one evaluation record.
Hours 1–24: contain and narrow the cause
Once the report is confirmed, reduce the blast radius while assigning an owner. Microsoft’s AI incident guidance describes immediate containment during the first hour, expanded and strengthened remediation during the first 24 hours, and source-level fixes over the following days or weeks. Those are response windows, not guarantees that a fix will be complete by the deadline.
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Hours 24–48: select a durable action and a verification plan
Use the evidence to choose among an evaluation correction, configuration change, retrieval or classifier change, model change, or platform escalation. Define the tests, metrics, and watch period that will determine whether the action worked. Do not close the incident because one rerun passes: model behavior can vary between runs and conditions.
How do I verify that the AI failure is real?
Validate the test and its dependencies before diagnosing the agent. Microsoft’s failure-triage playbook puts infrastructure and evaluation checks ahead of root-cause assignment because an unreachable data source or incorrect expected answer can make a healthy agent look broken.
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- Expected answer: Confirm that the reference answer is factually correct, current for the stated policy or product version, and specific enough for the task. If several answers are acceptable, make the grader recognize valid alternatives.
- Test case: Verify that the input represents a supported use case and that required context is present. Remove accidental ambiguity only after recording it; ambiguity may itself be the behavior you need to evaluate.
- Grader: Inspect the rubric, evaluator version, thresholds, and parsing rules. A formatting mismatch, brittle string comparison, or changed schema can create a false failure.
- Reproduction: Run the identical case again with the same model and configuration, then compare multiple runs. Record temperature, seed controls if available, tool settings, and time of execution.
Check knowledge, tools, and access
- Confirm that the knowledge source existed, was indexed, and was reachable by the agent at run time. Check document freshness, permissions, retrieval hits, and whether the expected passage was actually supplied to the model.
- Inspect API and connector health for timeouts, rate limits, quota exhaustion, schema changes, and partial responses. A tool call that silently returned no data is not evidence of a reasoning failure.
- Revalidate authentication, tokens, service accounts, and role permissions. An expired credential can produce a plausible but unsupported answer when the system falls back to its base model.
- Compare the deployed model, prompt, tool schema, retrieval index, and feature flags with the versions used to create the evaluation baseline.
A practical verification record
| Question | Evidence to capture | If the check fails |
|---|---|---|
| Was the expected output correct? | Reference source, policy version, accepted-answer rules | Classify as an evaluation setup issue until the case is corrected and rerun |
| Could the agent access required knowledge? | Index status, retrieval results, permissions, timestamps | Investigate retrieval, indexing, or access before changing the model |
| Did every dependency respond? | Connector logs, latency, status codes, rate-limit and timeout records | Contain dependency impact and involve the service owner |
| Was the run authenticated? | Credential validity, scopes, role changes, token errors | Restore access, then repeat the original test |
| Is the result reproducible? | Repeated outputs under recorded settings | Use a varied test set and treat nondeterminism as part of the risk |
Is this a bad prompt, a bad evaluation, or a platform problem?
Assign the primary owner to one of three buckets, while recording contributing causes separately. Microsoft’s playbook summarizes the operating principle as “Root cause → owner → action.” The bucket determines who can make the first meaningful change; it does not prevent several teams from sharing responsibility.
| Primary bucket | Typical evidence | First owner and action |
|---|---|---|
| Evaluation setup issue | The expected answer, rubric, parser, test data, or supported-use-case assumption is wrong | Evaluation author corrects the case, version-controls the change, and reruns the suite |
| Agent configuration issue | The agent behaves incorrectly, but its prompt, tool instructions, routing, retrieval settings, filters, or thresholds can be changed in configuration | Agent builder makes the narrowest safe change and tests for regressions |
| Platform limitation | The behavior persists across configurations and valid tests, or depends on an underlying service behavior unavailable to the application team | Platform or model team investigates; keep a containment control in place while awaiting a source fix |
Record compound causes instead of forcing a single answer
An expired connector token may explain missing context, while an overconfident prompt explains why the agent answered instead of asking for clarification. Mark one as the primary owner for action and list the other as a contributing cause. This preserves accountability without pretending that complex incidents have only one mechanism.
Questions that separate prompt from evaluation defects
- If a qualified human would accept several answers but the grader accepts one exact string, the evaluation is suspect.
- If the agent fails only when a tool is unavailable, inspect dependency handling and fallback instructions before changing the model.
- If the same valid case fails across independent configurations and models, escalate the shared data, platform, or task assumption.
- If a narrow prompt or routing change fixes the case without harming neighboring tests, configuration is the likely owner, subject to regression testing.
How should I assess the severity of an AI failure?
Do not rank an incident by failed-record count alone. Google’s incident process considers potential harm, affected data, service impact, and whether the problem is contained, then reassesses severity as facts change. Apply the same context-driven reasoning to an internal evaluation and a live deployment.
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Five dimensions to score
- Deployment domain: A failure in a high-consequence workflow deserves more urgency than the same output in a low-risk experiment.
- Affected population: Estimate users, customers, records, or decisions exposed, including whether the issue is spreading.
- Content or data harm: Consider privacy exposure, unsafe advice, discriminatory outcomes, security impact, financial loss, and irreversible actions.
- Business and service impact: Measure blocked work, incorrect transactions, contractual obligations, support load, and trust damage.
- Containment status: A confirmed issue still producing harmful outputs is more urgent than an equivalent issue isolated to a disabled test environment.
Use a severity statement that explains the decision
Write the rationale in one sentence that names the exposure and current control: for example, “High severity because the assistant is giving unsupported compliance guidance to a customer-facing group; traffic is restricted to a reviewed fallback while investigation continues.” Reassess that statement whenever scope, harm, or containment changes. Avoid inventing a universal P0–P3 scale; use your organization’s incident levels and attach this evidence to the selected level.
What can I do immediately to contain active harm?
Containment comes before a perfect explanation. Block known-bad inputs, add a safety filter, restrict access, disable an affected tool, route requests to a reviewed fallback, or require human approval for consequential outputs. These controls reduce exposure while the source of the failure is still being investigated.
First-hour containment
- Stop or narrow the workflow that is producing harmful or materially misleading outputs.
- Preserve logs, prompts, tool traces, retrieved documents, outputs, and user reports before changing the live path.
- Apply the smallest control that stops the known harm: input block, output filter, access restriction, rate limit, tool disablement, or safe fallback.
- Notify the incident commander and owners for the affected product, platform, security, privacy, legal, or compliance functions as the context requires.
- Record who approved the control, when it took effect, and what traffic remains exposed.
Expanded remediation in the first 24 hours
Strengthen the temporary control, test bypasses, expand monitoring, and determine whether neighboring workflows share the same failure mode. Compare the containment options by speed, blast-radius reduction, durability, owner required, verification burden, and regression risk.
| Response option | Time to containment | Durability | Main trade-off |
|---|---|---|---|
| Block or allow list | Usually fastest for known patterns | Low; inputs and adversaries change | Can miss variants and block legitimate work |
| Configuration or prompt change | Often quick and narrow | Moderate when the cause is local | May introduce instruction conflicts or regressions |
| Classifier or output filter | Moderate, depending on deployment path | Potentially durable for a defined harm class | Requires threshold tuning and false-positive testing |
| Retrieval or data-pipeline fix | Slower when reindexing or permissions are involved | High when missing or stale context is systemic | Needs data, access, and regression validation |
| Model or platform change | Slowest and dependent on another team | Potentially broad and durable | Large verification burden and wider regression risk |
Why temporary lists are not the final fix
A blocklist can stop a known phrase today but will not address a changing input distribution or an adversary’s variant. Microsoft’s incident guidance treats tactical controls as containment and emphasizes a systemic source fix for lasting protection.
What should an AI failure log contain?
A complete log lets a new responder reconstruct the event without relying on memory. NIST root-cause guidance calls for the timeline, missed warning signs, key decisions, gaps, mitigations, and verification of effectiveness; include those items alongside the technical trace.
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- Report identifier, reporter, exact input, expected output, observed output, and any user or business consequence
- Model, prompt, agent, evaluator, retrieval index, tool, feature-flag, and configuration versions
- Dependency and connector health, authentication state, latency, status codes, rate limits, and retrieved context
- Timeline of detection, reproduction, escalation, containment, changes, and recovery
- Severity level and rationale, affected users or data, geographic or business scope, and whether exposure is continuing
- Primary owner, contributing causes, incident commander, consulted technical, product, security, privacy, legal, or compliance leads
- Containment action, approval, start time, expected side effects, and rollback method
- Source fix, evaluator version, test-set identity, rerun results, failed neighboring cases, and watch-period observations
- Missed warning signs, decisions that delayed response, process gaps, and follow-up actions with owners and dates
Keep evidence immutable and changes attributable
Store the original failing trace separately from edited prompts or corrected labels. Every change should show the author, timestamp, reason, and rollback path. If privacy rules limit raw content, retain a controlled reference or redacted artifact that still lets authorized responders verify what happened.
Who should command the incident?
Assign a named incident commander as soon as the failure is confirmed. Google documents a flow in which an on-call responder evaluates the report and severity, assigns the commander, and brings in product, technical, and legal leads as needed. The commander owns coordination and decisions; they do not have to be the person who writes the fix.
Suggested roles
- Incident commander: Sets priorities, approves containment, maintains the timeline, and decides when evidence supports a severity change or closure.
- Technical lead: Reproduces the issue, traces tools and dependencies, and proposes configuration, data, model, or platform actions.
- Evaluation lead: Validates the test case, grader, reference answer, and rerun design.
- Product and domain lead: Explains user impact, acceptable behavior, and business fallback requirements.
- Security, privacy, legal, or compliance leads: Join when data exposure, regulated decisions, contractual duties, or reporting obligations are possible.
Keep consequential actions human-approved
AI can summarize logs, cluster similar failures, or draft a timeline, but it should not silently apply a consequential remediation. Google states that generated action payloads must pass validation and receive explicit human-in-the-loop confirmation before application. Require a person to approve access changes, customer communications, model rollbacks, data deletion, or other irreversible actions.
How do I choose the right layer to fix?
Match the remedy to the confirmed owner and the breadth of the cause. A local configuration error should not trigger a platform migration; a shared platform defect should not be hidden behind dozens of application-specific patches.
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Evaluation setup
Correct the expected answer, rubric, parser, test data, or supported-use-case definition. Version the correction and rerun both the affected case and a sample of cases that previously passed. Do not call the agent “fixed” merely because the score rose after making the grader more permissive.
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Agent configuration
Change one narrow variable at a time where practical: instruction hierarchy, tool schema, routing condition, retrieval parameters, refusal rule, or confidence threshold. Keep the containment control until the new configuration passes targeted safety, accuracy, and regression tests.
Retrieval, classifier, or data pipeline
Repair indexing, freshness, chunking, permissions, labels, classifier thresholds, or fallback behavior when the evidence points to missing context or misclassification. Test stale, contradictory, empty, and unauthorized-source conditions, not only the ideal document.
Model or platform
Escalate when the failure persists across valid configurations or depends on a shared service behavior outside the application team’s control. Supply the reproducible trace, scope, severity, attempted mitigations, and verification results so the platform team can act without restarting the investigation.
How do I verify that an AI fix really worked?
Verification is a new evaluation, not a visual inspection of one improved answer. Re-run the original failure, nearby variants, known-safe cases, and adversarial or boundary inputs under the deployed configuration. If one score improves while another worsens, investigate instruction conflicts, threshold changes, retrieval trade-offs, or a shifted error distribution.
A four-part verification set
- Exact regression: Repeat the original input and confirm the harmful behavior is gone.
- Variant coverage: Change wording, ordering, language, context length, tool availability, and user role to test whether the fix is pattern-specific.
- Neighboring behavior: Recheck cases that were previously correct, including refusals, citations, latency, cost, and required formatting.
- Operational path: Test the real authentication, retrieval, connectors, fallback, logging, and human-approval flow used in deployment.
Use a watch period
Keep monitoring after the rerun because model behavior is nondeterministic and production inputs evolve. Define the observation window, sample size or traffic coverage, alert thresholds, owner, and rollback trigger before reopening normal traffic. Close the incident only when the evidence shows both corrected behavior and effective monitoring.
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What are the most common triage mistakes?
- Editing the prompt before checking the grader: This can make a valid agent worse while leaving the evaluation defect intact.
- Counting failures without context: Ten low-risk test misses may matter less than one harmful answer in a high-consequence workflow.
- Calling an outage a model problem: Timeouts, expired credentials, empty retrieval results, and rate limits can all produce model-like symptoms.
- Leaving the temporary filter in place indefinitely: A brittle list hides the source defect and accumulates maintenance risk.
- Changing several layers at once: Without versioned, isolated changes, you cannot tell what worked or what introduced a regression.
- Closing after one passing run: Nondeterminism and changing inputs require varied tests and post-fix observation.
- Letting automation take irreversible action: Generated remediation payloads still need validation and explicit human approval.
What should the 48-hour decision checklist say?
Use this as the handoff criterion between investigation and repair:
- The original report, expected answer, observed output, grader, and dependencies have been verified.
- The primary bucket—evaluation setup, agent configuration, or platform limitation—is named, with contributing causes listed.
- Severity explains domain, affected population, harm, business impact, and containment status.
- Active harm is blocked, restricted, filtered, or routed to a safe fallback, with approval and rollback recorded.
- A named incident commander and required domain, technical, product, and legal or security owners are engaged.
- The selected fix layer has an owner, change record, verification set, regression plan, and watch period.
- Human approval is required for consequential actions, and closure depends on monitoring evidence rather than a single pass.
Frequently Asked Questions
How long should an AI incident stay in containment?
Keep containment until the source fix passes the agreed regression set and the watch period shows no recurrence at the defined alert threshold. If the fix depends on another team or release window, document the residual exposure and renew the temporary control rather than removing it on schedule.
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Pause automated closure and inspect the rubric, accepted-answer policy, and reviewer instructions. Preserve the disagreement as a labeled case; it may indicate an ambiguous task, an evaluator calibration problem, or a genuine model error that a single score conceals.
Should I change the model or the prompt first?
Change the narrowest layer that matches the evidence. A prompt or routing change is appropriate for a local configuration defect, while a failure that survives valid configurations and shared dependency checks should be escalated instead of patched repeatedly.
How can I test an AI fix when production data is sensitive?
Use redacted or synthetic cases that preserve the relevant structure, plus access-controlled replay of a small approved sample. Record which production characteristics were removed so reviewers understand what the verification does and does not establish.
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