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You can produce a solid first draft of an SRE postmortem in about five focused minutes with AI, but only if the facts are already collected and the model is restricted to extracting and organizing them. The safe method is to assemble a sanitized incident packet, have AI build a UTC timeline with source labels, generate the standard report sections, propose specific owned actions, and then have responders verify every sentence. Five minutes is a drafting-workflow target, not a published performance benchmark. AI must not invent events, declare a root cause without evidence, expose secrets, or replace the engineers who operated the service.
What does “write a postmortem in five minutes” actually mean?
It means reducing the mechanical work of collecting notes, normalizing timestamps, and filling a known structure. It does not mean investigating a complex outage, proving causality, obtaining approvals, or publishing a finished report in five minutes. Those activities still require the people who handled the incident and understand the system.
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A postmortem is a learning and change mechanism, not a disciplinary record. Google SRE describes a blameless document as one that assumes “everyone involved in the incident had good intentions and did the right thing with the information they had.” The useful output explains customer impact, what happened, how the team detected and responded, why safeguards failed, and which changes will reduce recurrence.
Use the five-minute sequence below after service has stabilized and the relevant evidence is available. If the packet is incomplete, the honest result is a short draft containing explicit unknowns, not a polished fiction.
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How do I prepare the incident packet in minute 0–1?
Spend the first minute assembling only approved, sanitized material. AI can organize evidence it receives; it cannot recover missing evidence reliably. Put a source label beside every item so a reviewer can trace each statement back to its origin.
Include these fields
- Incident identifier and severity classification.
- UTC start and end times, including the time zone conversion used for any original notes.
- Customer or internal impact, with measured scope and duration.
- Alert, dashboard, log, trace, ticket, and change-record links.
- Deployments, feature-flag changes, configuration edits, dependency events, and rollback times.
- Responder notes and relevant chat excerpts, with personal data and secrets removed.
- Metrics or third-party status pages that substantiate each major event.
Keep raw evidence separate from interpretation. A line such as “database saturation reached 92% at 14:07 UTC” is evidence; “the database caused the outage” is a hypothesis until the team demonstrates the causal chain. Attach the metric or log reference to the first line and leave the second for the analysis section.
Sanitize before sending anything to a model
Remove credentials, tokens, private keys, customer names, email addresses, request payloads containing personal data, and proprietary code that is not needed for the report. Replace identities with roles such as “on-call engineer” or “release manager.” Confirm that your organization’s AI policy allows the chosen service and define how prompts and outputs are retained.
What prompt should I use to extract a trustworthy timeline in minute 1–2?
Ask for extraction, normalization, and uncertainty markers—not a narrative. A constrained prompt makes unsupported embellishment easier to detect and follows the incident-timeline practice recommended by PagerDuty and Atlassian: use UTC and attach metrics or source pages to events.
Extract only claims directly supported by the incident packet below.
Produce a UTC timeline. For every event, include:
- timestamp (UTC)
- actor or system
- action
- observed result
- source label or link
Mark missing values as UNKNOWN. Do not infer intent, causality, or
unstated timestamps. Preserve conflicting observations in separate entries.
After the timeline, list contradictions and unanswered questions.Paste the packet beneath that instruction, retaining the source labels. Require the model to repeat an event’s evidence rather than silently merge similar messages. If two dashboards disagree, keep both readings and flag the conflict for a human; do not ask the model to choose the “most likely” value.
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Why UTC and source labels matter
Responders often work across local time zones, daylight-saving changes, and systems with different clock settings. Converting every event to UTC gives the report one ordering that another team can audit. A source label lets a reviewer open the associated metric, log, ticket, or status page and check whether the event actually occurred. Remove a source only when it contains sensitive data, and then identify the owning system and retention location instead.
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Give the model a fixed template and keep confirmed facts separate from hypotheses. Atlassian’s current template covers the lead-up, fault, impact, detection, response, recovery, and timeline; Google SRE frames the purpose as documenting impact, actions, causes, and preventive actions.
Use these sections
- Summary: two or three sentences stating the service, customer effect, duration, and current status.
- Lead-up: relevant releases, configuration changes, dependency conditions, or capacity trends before the incident.
- Fault and impact: the observed failure mode and measured customer or internal consequences. Keep “fault” descriptive until causality is established.
- Detection: the alert, symptom, or report that first revealed the problem, including detection time and any delay.
- Response: decisions, mitigations, escalations, and communication steps in chronological order.
- Recovery: the change that restored service, validation performed, and remaining risk.
- Root-cause analysis: confirmed causes, contributing conditions, and clearly labeled hypotheses that still need testing.
- Lessons: what the team learned about architecture, procedures, observability, or training.
- Follow-up actions: one verifiable action for each important failure mode.
Use a second instruction after the extraction: “Populate these sections using only confirmed claims from the packet. Put hypotheses under a heading named ‘Unconfirmed hypotheses.’ Preserve UNKNOWN markers. Do not assign blame to a person or team.” This prevents a fluent paragraph from turning an assumption into a fact.
Keep the summary measurable
Replace vague language such as “users were affected” with the measured region, product surface, error rate, request count, or duration available in the packet. If the packet does not establish a number, write that the scope is still being quantified and create an action to obtain it. A precise limitation is more useful than a guessed percentage.
How do I generate useful action items in minute 3–4?
Ask AI to map each failure mode to one concrete action, then have the service owner approve and prioritize it. An action item without an owner, a due date or service-level objective, and a verification signal is a wish rather than a reliability control.
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For each confirmed failure mode, propose one action.
Include: owner role, due date or agreed SLO, expected reliability effect,
and verification signal (test, metric, alert, or review).
Do not use vague wording such as “improve monitoring.”
If no owner or deadline is established, mark it UNKNOWN.Examples of specific wording include “Add an alert for queue age above the documented threshold, owned by the messaging team, verify with a test incident, due in four weeks.” The exact threshold and owner must come from your service’s agreements; AI should not invent them.
Atlassian’s current process guidance uses four- or eight-week SLOs for priority actions, depending on the service. Google SRE recommends that agreed action-item SLOs feed back into the team’s backlog. Copy approved items into the system where work is actually tracked, link the ticket to the postmortem, and record completion evidence there.
What must a human verify in minute 4–5?
Compare every generated sentence with the packet and with responder knowledge. The final five-minute pass is a fact check and a blameless edit, not a ceremonial approval.
- Check every timestamp, duration, metric, and sequence against the original source.
- Remove causal verbs such as “caused” or “because” unless the evidence and engineering review support them.
- Separate confirmed causes from hypotheses and record what test would confirm or reject each hypothesis.
- Delete secrets, customer-identifying data, and unnecessary personal details.
- Rewrite language that judges a person (“failed to notice”) as a system description (“the alert did not fire under the observed condition”).
- Confirm that mitigation and recovery claims include the change made and how restoration was verified.
- Ensure each action has an accountable owner, a deadline or SLO, priority, and a verification signal.
Google SRE also emphasizes that an honest, timely write-up reviewed by stakeholders and shared broadly is key to finding effective corrective actions. “Broadly” still means within your organization’s privacy and security boundaries; redact or restrict the report when the evidence contains regulated data.
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Replace blame with operational context
- Instead of “the engineer deployed a bad change,” write “the deployment passed the available checks, but those checks did not cover the dependency condition observed in production.”
- Instead of “the team ignored the alert,” write “the alert routed to a queue that was not staffed during the event.”
- Instead of “someone should have known,” write “the runbook did not document this failure mode.”
Ask the model to preserve the decisions people made with the information available at that moment. Do not allow hindsight to turn an uncertain choice into negligence. Review the text with the responders who were present; they can correct missing context that no prompt can supply.
Make uncertainty actionable
Use explicit labels such as UNKNOWN, “unconfirmed hypothesis,” and “evidence needed.” Pair each unresolved item with an investigation or instrumentation task. This keeps the report honest while giving the team a path to improve it.
What can go wrong when AI writes the postmortem?
The most common failures are fluent inventions, false precision, privacy leakage, and action items that never enter execution. A short review checklist catches each one.
- Invented events: the model fills a gap between two chat messages. Require source labels and UNKNOWN markers.
- False root cause: correlation is presented as causation. Keep hypotheses separate until an engineer validates the mechanism.
- Timestamp drift: local times are mixed with UTC. Normalize during extraction and show the time zone in every source record.
- Over-compression: a summary hides a detection delay or failed mitigation. Compare it with the full timeline and impact measurements.
- Privacy exposure: raw chats contain customer or credential data. Sanitize before prompting and inspect the output for echoes.
- Vague remediation: “improve monitoring” has no test or owner. Require a failure mode, accountable role, deadline, and verification signal.
- Untracked work: approved actions remain only in the document. Create backlog tickets and link completion evidence.
How should I choose an AI or incident workflow for this process?
Judge the workflow by evidence fidelity and follow-through, not by how polished its prose sounds. A suitable system lets you preserve source references, control sensitive data, and connect approved actions to operational work.
| Decision area | Questions to ask |
|---|---|
| Evidence fidelity | Can each timeline event retain a metric, log, ticket, or source-page reference? |
| Blamelessness and privacy | Can you remove blameful language and customer data before storage or prompting, and control retention? |
| Speed | Does automation handle collection and formatting without hiding UNKNOWN values or conflicts? |
| Follow-through | Are owners, due dates or SLOs, approvals, priorities, and backlog tickets connected? |
| Reuse | Can structured metadata support searching and trend analysis across incidents? |
A general model can draft text, while an incident-management workflow may add templates, approvals, and action tracking. Whichever you use, keep the evidence packet and final report under your organization’s access, retention, and deletion rules.
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When should the postmortem be reviewed and published?
Start the draft while evidence is fresh, then follow your service’s review deadline. Atlassian’s current process guidance calls for conducting the review or draft within 24–48 hours and no more than five business days after resolution. That window is a process target, not permission to publish unverified claims.
Invite incident responders, the service owner, security or privacy reviewers when appropriate, and stakeholders who can approve actions. Record disagreements as unresolved questions rather than silently choosing a narrative. After approval, share the report with the organization or relevant engineering groups, with redactions required by policy.
Track the document as a living control
Close an action only when its verification signal is recorded: a test passed, an alert fired in a controlled exercise, a runbook was reviewed, or a metric changed as expected. Reopen the item if the signal fails. Periodically search reports for recurring failure modes, detection gaps, and overdue actions; those trends often justify broader reliability work than any single incident.
What does the five-minute workflow look like in practice?
Suppose a deployment changed a timeout, an alert fired at 14:07 UTC, and responders rolled back at 14:19 UTC. The packet contains the deployment record, alert link, error-rate dashboard, rollback command, and a customer-impact ticket. AI can normalize those records into four timeline entries, draft the impact and response sections, and identify a hypothesis that the timeout exposed a slow dependency. It must leave the dependency claim unconfirmed until traces or a controlled test demonstrate it. The human reviewer then verifies the 12-minute duration, removes any customer identifiers, assigns an owner to add dependency-latency coverage, and links that work to a four- or eight-week SLO if the service’s process requires one.
Five-minute postmortem checklist
- Assemble an approved, sanitized packet with source labels.
- Normalize all times to UTC and preserve conflicting observations.
- Prompt for extraction and UNKNOWN markers, not a story.
- Fill summary, lead-up, fault, impact, detection, response, recovery, causes, lessons, and actions.
- Keep hypotheses visibly separate from confirmed causes.
- Give every action an owner, deadline or SLO, priority, and verification signal.
- Fact-check every sentence, remove secrets and blame, and obtain stakeholder review.
- Publish within the agreed review window and track actions in the team backlog.
Frequently Asked Questions
Can AI determine the root cause of an outage by itself?
No. It can cluster evidence and suggest hypotheses, but root-cause claims require engineers to establish a causal mechanism and validate it with logs, traces, metrics, or tests. Treat any model-generated explanation as unconfirmed until that evidence is recorded.
Should I paste an entire incident chat into an AI tool?
Only after removing credentials, personal data, customer identifiers, and irrelevant sensitive material, and only when the tool’s retention and access settings meet your policy. A curated packet usually produces a more auditable report than an unfiltered transcript.
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Keep each supported observation as a separate timeline entry, label the sources, and add the conflict to an unanswered-questions list. Resolve it with system logs or clock information rather than asking the model to select the most plausible version.
How can I tell whether postmortems are improving reliability?
Review completion and verification rates for action items, recurring failure modes, detection time, mitigation time, and repeat incidents. Trend those measures across reports; a beautifully written document without completed, tested actions has not changed reliability.
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