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You can produce a useful first draft of an SRE postmortem in five minutes with AI, but only if you treat the model as an evidence organizer—not an investigator or judge. The reliable workflow is to assemble a sanitized incident packet, make AI extract a UTC timeline with a source beside every event, have it populate a proven postmortem structure, turn each failure mode into a tracked action, and then have responders verify every sentence. Five minutes is a drafting target for this workflow, not a published speed benchmark. Human responders still own factual accuracy, privacy, causal conclusions, approvals, and the final blameless wording.
What is an SRE postmortem supposed to accomplish?
A postmortem is a learning and change mechanism, not a punishment document. It records what customers experienced, how the system behaved, what responders did, why the failure occurred, and which changes will reduce the chance or impact of recurrence. Google’s SRE guidance describes the purpose as documenting impact, actions, causes, and preventive actions, then using the agreed actions to improve the service.
A useful report connects six evidence streams:
- Impact: affected users, requests, regions, data, and duration.
- Timeline: a UTC sequence of alerts, changes, observations, decisions, and recovery.
- Detection: how the issue was discovered and what monitoring missed or delayed.
- Response: investigations, mitigations, escalations, and communications.
- Causes: confirmed contributing conditions, with unverified explanations kept separate.
- Prevention: owned, prioritized actions with a way to verify their effect.
Google’s blameless principle is precise: “A blamelessly written postmortem assumes that everyone involved in the incident had good intentions and did the right thing with the information they had.” That framing makes the document safe enough for people to disclose weak signals, confusing procedures, and design gaps—the information needed for reliability work.
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It is realistic for a structured first draft when the incident material already exists in accessible systems. It is not realistic to investigate an ambiguous outage, establish root cause, obtain approvals, and publish a trustworthy report in five minutes. The clock covers collection, extraction, formatting, action drafting, and an initial human check; it does not replace engineering analysis or stakeholder review.
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Keep the five-minute claim honest by measuring the scope of work:
| Within five minutes | Still requires responders |
|---|---|
| Collecting approved evidence, normalizing timestamps, filling sections, and proposing actions | Confirming facts, determining causality, assessing privacy, approving wording, and tracking work |
| Producing a readable draft with explicit UNKNOWN markers | Resolving each UNKNOWN from logs, metrics, tickets, or subject-matter experts |
| Linking events to the sources supplied in the packet | Checking that links are accessible, retained, and appropriate for the report’s audience |
Do not present the result as an automated incident verdict. A model can compress information and expose gaps; it cannot know whether a plausible explanation is true without evidence.
How do I assemble the incident packet in minute one?
Start with a small, approved packet rather than an unfiltered export. Every item should carry a source label so a reviewer can retrace the statement later.
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- Identify the incident: incident ID, service, severity, and the UTC start and end (or the fact that an endpoint is still unknown).
- Describe customer impact: affected functionality, geography, request volume, error rate, data integrity, and the number or class of customers if approved for sharing.
- Add detection evidence: alert names, firing and recovery times, dashboards, synthetic checks, and the first human report.
- Capture changes: deployments, feature-flag edits, configuration changes, dependency events, capacity changes, and rollbacks around the lead-up.
- Include responder material: notes, chat excerpts, commands or decisions, escalation records, status updates, and tickets.
- Label each source: put a short identifier and link or system reference beside every item, such as
metric: checkout-5xxorticket: INC-2041.
Sanitize before sending anything to an AI system. Remove credentials, tokens, private keys, internal network details that the audience does not need, customer names, email addresses, payloads containing personal data, and confidential contract terms. Preserve the technical facts needed to reason about sequence and impact. If your organization has a retention or approved-model policy, use only the model and workspace allowed by that policy.
What prompt makes AI extract facts instead of inventing a story?
Use an extraction prompt before asking for prose. This forces the model to separate evidence from interpretation and makes missing information visible.
Extract only claims directly supported by the incident packet below. Produce a UTC timeline. For every event include: timestamp, actor or system, action, observed result, and source label or link. Mark anything not established by the packet as UNKNOWN. Do not infer root cause, intent, or chronology from context. Preserve conflicting observations as separate entries and flag the conflict.Paste the packet after the instruction and keep the source label adjacent to each item. PagerDuty’s timeline guidance recommends attaching metrics or third-party pages to entries, while Atlassian’s guidance uses UTC timestamps so people in different regions read the same sequence. Ask the model to retain the original precision: if a note says “around 14:00 UTC,” do not let it manufacture seconds.
Review the extraction before drafting. Check that an alert firing is not described as customer impact, that a mitigation is not described as a root cause, and that recovery is supported by a metric or explicit observation rather than by the end of a chat thread.
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What should the AI-generated SRE postmortem include?
Once the fact table is sound, ask AI to fill a template whose sections match the questions reviewers need answered. Atlassian’s template names the lead-up, fault, impact, detection, response, recovery, and timeline; Google’s framework adds causes and preventive actions.
Executive summary
In a few sentences, state the service, UTC duration, customer effect, and the immediate mitigation. Do not put an unconfirmed explanation in this paragraph.
Lead-up and fault
Describe relevant system conditions and changes before the incident, then the failure mode that is directly evidenced. Distinguish “a deployment changed the timeout” from “the deployment caused the outage” unless the causal link has been established.
Impact
Give measured scope and uncertainty: affected requests, users, regions, data, and duration. Identify what was not affected when that boundary is useful.
Detection
Record the first signal, alert latency, and any gap between technical failure and customer detection. Include the metric or report that supports each time.
Response and recovery
List decisions, mitigations, escalations, communications, and the observation that showed recovery. Keep the actor as a team, role, or system when an individual name is unnecessary.
Root-cause hypotheses
Keep hypotheses in a visibly separate section until an engineer confirms them. For each hypothesis, state supporting evidence, contradictory evidence, and the next check required. A plausible narrative is not a confirmed root cause.
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Lessons and follow-up
Convert each identified failure mode into a specific action with an owner, due date or service-level objective, expected reliability effect, and verification signal.
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A timeline is auditable when every row answers who or what acted, what happened, and where the fact came from. Use a table with one event per row rather than a paragraph that blends observations and conclusions.
| Field | What to record | Example |
|---|---|---|
| Timestamp | UTC time and the precision supported by evidence | 2026-10-26 14:03 UTC |
| Actor or system | Service, alert, responder role, or external dependency | API gateway alert |
| Action or event | Deployment, alert, command, decision, or customer report | 5xx rate exceeded threshold |
| Observed result | Measured or explicitly reported outcome | Checkout errors increased to 18% |
| Source | Metric, log, ticket, dashboard, chat message, or status page reference | metric: checkout-5xx |
If two sources disagree, retain both entries and flag the conflict for review. Do not “smooth” the timeline by choosing the version that makes the response look cleaner. Unknown timestamps, actors, and results should remain UNKNOWN until someone resolves them.
How do I generate action items that actually improve reliability?
Prompt for one action per failure mode, not a list of aspirations. Require five fields: owner, due date or SLO, expected reliability effect, verification signal, and tracking location.
| Weak item | Actionable item |
|---|---|
| Improve monitoring | Service owner adds a regional checkout-success alert by 2026-11-15; verify with a simulated error test and a documented alert response. |
| Fix deployment process | Release engineering adds an automatic rollback threshold for the affected metric, owned by the release lead, with verification in the next two production releases. |
| Review capacity | Infrastructure owner models peak traffic and raises the headroom SLO; attach the capacity report and review it at the next planning checkpoint. |
Google’s guidance says agreed action-item SLOs should feed back into the team backlog. Atlassian documents four- or eight-week SLOs for priority actions depending on the service. Use the interval your service has adopted, record exceptions explicitly, and connect each item to a ticket or backlog entry so completion is visible outside the postmortem.
How do I fact-check and keep the draft blameless in minute five?
Read the draft sentence by sentence against the packet. A reviewer should be able to point to evidence for every factual statement and identify every remaining UNKNOWN.
- Check chronology: verify UTC conversion, start and end boundaries, and the order of alerts, changes, decisions, and recovery.
- Check quantities: confirm rates, counts, durations, and regions against the cited metric or ticket; preserve qualifiers such as estimated or partial.
- Remove unsupported causality: replace “X caused Y” with “X preceded Y” or “the team is testing X as a hypothesis” when evidence is incomplete.
- Remove blame: describe system conditions, procedures, and information available at the time instead of accusing a person or team.
- Protect privacy and security: delete secrets and customer-identifying details that are not required for learning.
- Validate actions: confirm each owner, due date or SLO, priority, and verification signal with the responsible team.
Google’s wording is a useful test: assume good intentions and focus on improving systems, procedures, and training. The final report should make it easier to discuss decisions made under pressure, not harder.
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When should the postmortem be reviewed and published?
Begin the review while details are fresh. Atlassian’s current process guidance calls for conducting the review or draft within 24–48 hours after resolution and no more than five business days later. That is a process target, not permission to publish an unverified account.
Invite responders, service owners, and stakeholders who can validate impact, causality, and action ownership. Google notes that an honest and timely postmortem reviewed by stakeholders and shared broadly is key to identifying effective corrective actions. Share according to your organization’s access rules: broad learning is valuable, but restricted customer or security information must remain protected.
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Evaluate the workflow, not just the model’s prose. The following questions expose whether speed is being purchased by hiding uncertainty.
| Evaluation axis | Questions to ask |
|---|---|
| Evidence fidelity | Can every timeline event link to a metric, log, ticket, or source page? Are conflicts and UNKNOWN values preserved? |
| Blamelessness and privacy | Does the process remove blameful language and sensitive customer data before publication? |
| Speed | Which collection and formatting steps are automated, and can a reviewer see what the model inferred? |
| Follow-through | Are owners, due dates or SLOs, approvals, and backlog tickets connected? |
| Reuse | Does structured metadata support searching incidents and spotting recurring failure modes? |
A lightweight prompt-and-template workflow can work when your evidence systems are stable and your team is disciplined about review. A managed incident process may be preferable when you need built-in approvals, access controls, timeline capture, and action tracking. In either case, keep the incident evidence and the final reviewed report linked so a later investigator can reconstruct how a conclusion was reached.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What commonly goes wrong, and how do I fix it?
The model invents timestamps or causes
Symptom: the draft contains precise times or a confident root cause absent from the packet. Fix: use the extraction prompt, require UNKNOWN markers, and reject any sentence without a source.
The timeline is a narrative with no audit trail
Symptom: reviewers cannot tell which metric or note supports an event. Fix: require a source column and preserve links or system identifiers beside every row.
The report names a person as the problem
Symptom: wording blames an operator for a decision made with limited information. Fix: describe the system condition, procedure, signal quality, and available options; move individual names to operational records only when necessary.
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What is the practical five-minute checklist?
- Minute 0–1: assemble the approved packet and label every source.
- Minute 1–2: extract a UTC, source-linked timeline with UNKNOWN markers.
- Minute 2–3: populate summary, lead-up, fault, impact, detection, response, recovery, hypotheses, lessons, and actions.
- Minute 3–4: create one owned, time-bound, verifiable action for each failure mode.
- Minute 4–5: fact-check, remove blame and secrets, separate hypotheses, and route the draft for stakeholder review.
This sequence makes AI useful where it is strongest—extraction, normalization, and drafting—while keeping truth, privacy, causal judgment, and publication with the responders responsible for the service.
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Frequently Asked Questions
Can an AI model determine the root cause of an outage from logs alone?
Not reliably. It can cluster clues and propose hypotheses, but causal attribution needs engineers to test alternatives against metrics, changes, dependencies, and reproduction evidence. Label every model-generated explanation as a hypothesis until a responder confirms it.
Should customer names or full chat transcripts go into a postmortem prompt?
Usually no. Provide the minimum sanitized facts needed to establish impact and sequence, and retain sensitive originals in the approved incident system. Record that data was redacted so readers understand the report’s boundaries.
How can I compare two postmortem tools or workflows?
Score each one on source-linked evidence, UTC timeline handling, privacy and blameless editing, action ownership and SLO tracking, approval controls, and the ability to search structured incident data later. A polished summary without traceable evidence is not a reliable workflow.
What should happen when an action item misses its SLO?
Keep the original item visible, record the reason for the miss, and have the owner or service lead reset the date or escalate the priority. Closing an overdue item without a verification signal hides the reliability gap.
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