Incident postmortem template for engineering teams
When production breaks, there are only two jobs: fix it fast and make sure it does not break the same way again.
Why this template works for dev teams
Keeps it blameless
Focus on systems and processes, not individuals, so engineers feel safe being honest about what happened.
Forces systematic analysis
Use 5 Whys and timeline reconstruction to move from symptoms to underlying system failures.
Turns incidents into action
Every postmortem ends with owned, dated action items tracked like regular work—not forgotten in a doc.
How to run a blameless incident postmortem
Schedule within 48-72 hours
Schedule when memories are fresh but the immediate fire-drill has passed. The postmortem is a written document first; the meeting reviews it.
Write a neutral summary
Start with what broke, when, how long, and user impact. Keep it factual and blame-free.
Reconstruct the timeline
Use logs, alerts, and chat transcripts to build a minute-by-minute timeline from detection to resolution so everyone agrees on the facts.
Run root cause analysis
Use 5 Whys to dig from surface symptoms to underlying failures in process, architecture, or org structure.
Define action items with owners
Turn insights into concrete fixes and improvements with single owners and realistic deadlines. Track them like any other work.
Common mistakes to avoid
Blaming individuals instead of systems
Saying "Alice deployed bad code" makes engineers defensive and hides real causes in process, tools, or architecture.
Why it matters: Blameless culture enables honest learning.
Writing vague timelines
Incomplete timelines lead to arguments over perception instead of learning from actual decision points.
Why it matters: Clarity drives understanding.
Stopping at surface symptoms
Stopping at "service crashed" instead of digging into why safeguards, reviews, or tests failed to prevent it.
Why it matters: Surface fixes create repeat outages.
Letting action items drift
Action items without owners and deadlines are the #1 reason the same class of incidents keeps happening.
Why it matters: Follow-through prevents repeat failures.
Incident postmortem template
Copy this template and paste it into HighFly, GitHub, Notion, or your preferred tool.
# Incident Postmortem — [System / Service Name]
**Incident ID:** [INC-YYYY-MM-###]
**Date of incident:** [YYYY-MM-DD]
**Severity:** [SEV1 / SEV2 / SEV3]
> Define severity based on user impact, not internal stress level.
> Tip: If everything is SEV1, nothing is SEV1.
**Lead responder:** [Name]
**Authors:** [Names]
**Postmortem date:** [YYYY-MM-DD]
---
## 1. Summary
Provide a brief, neutral summary of the incident in 2–3 sentences.
- What broke?
- When did it happen?
- How long did it last?
- What was the user / business impact?
**Example:**
On 2025-01-08 from 14:23–15:10 UTC, checkout requests failed for ~4% of users due to database connection pool exhaustion in the recommendation service. 342 orders failed on first attempt but were later recovered via retries.
---
## 2. Impact
### 2.1 User impact
- Affected users: [e.g. 4% of active sessions / 3.4k users]
- Affected regions: [e.g. US-East, EU-West]
- User-visible symptoms: [e.g. 500 errors on checkout, slow page loads]
### 2.2 Business impact
- Duration: [e.g. 47 minutes]
- Estimated revenue impact: [e.g. $8,400 in delayed or failed orders]
- SLA impact: [e.g. breached 99.9% uptime for checkout in Jan]
- Escalations: [e.g. 12 support tickets, 1 enterprise escalation]
---
## 3. Timeline
Reconstruct the sequence from first signal to full resolution.
| Time (UTC) | Event | Source / Owner |
|-----------:|----------------------------------------------------|-------------------------|
| 14:23 | Alert fired for checkout latency (p99 > 8s) | Monitoring / Datadog |
| 14:26 | On-call paged and acknowledges incident | Pager / On-call engineer|
| 14:28 | Error rate and DB connections inspected | On-call engineer |
| 14:31 | Incident channel created, roles assigned | Incident lead |
| 14:34 | Recent deployment in recommendation service found | Deploy logs |
| 14:38 | Decision made to roll back deployment | Incident lead + team |
| 14:42 | Rollback completed, error rate returns to baseline | CI/CD |
| 15:10 | Incident resolved, status page updated | Incident lead |
> Link to dashboards, logs, and chat threads where relevant.
---
## 4. Root cause analysis
Describe why this happened, not just what happened. Use a simple method like "5 Whys".
### 4.1 Primary technical cause
- Immediate cause: [e.g. DB connection pool exhausted at 500/500 connections]
- Symptom: [e.g. application threads blocked waiting for connections, causing timeouts]
### 4.2 Why did this happen?
1. **Why 1:** [e.g. New recommendation feature opened multiple DB connections per request and did not release them efficiently.]
2. **Why 2:** [e.g. Feature was shipped without load testing at expected peak traffic.]
3. **Why 3:** [e.g. Load test environment is under-provisioned and not trusted, so teams skip it.]
4. **Why 4:** [e.g. There is no mandatory pre-deploy performance check for services touching the database.]
**Underlying system failure:**
[Summarize the deeper issue, e.g. "Our deployment process does not enforce performance checks for DB-intensive features, and our staging environment is not production-like."]
### 4.3 Contributing factors
List other factors that made the impact worse or detection slower.
- [e.g. Alert thresholds on DB connections were set too high (95% instead of 70%).]
- [e.g. No clear runbook for "DB connection exhaustion," so responders improvised.]
- [e.g. Feature flags were not used, so rollback required full redeploy.]
---
## 5. Detection & response
### 5.1 Detection
- How was the incident first detected? [alert, customer report, internal user]
- Was detection timely? If not, why?
- Were there missing or noisy alerts?
### 5.2 Response
- Who was involved? [roles: on-call, incident lead, comms, product]
- What worked well in coordination and communication?
- Where did confusion or delay occur?
---
## 6. What went well
Capture strengths to preserve in future incidents.
- [e.g. On-call acknowledged the alert within 3 minutes and started an incident channel.]
- [e.g. Logs and metrics gave enough context to identify the faulty service quickly.]
- [e.g. Rollback playbook was clear and executed without issues.]
---
## 7. What could be improved
Focus on process, tooling, and documentation improvements.
- [e.g. Alerts should fire earlier on DB saturation to avoid user-visible errors.]
- [e.g. We need a standard load-testing step for DB-intensive changes.]
- [e.g. Runbooks for common failure modes are missing or out of date.]
---
## 8. Action items
Turn findings into concrete changes with owners and deadlines. Track these like regular work.
| Action item | Type (Fix/Improve) | Owner | Due date | Status |
|----------------------------------------------------------|--------------------|--------|------------|---------------|
| Add DB connection saturation alert at 70% utilization | Fix | [Name] | [YYYY-MM-DD] | ☐ Not started |
| Upgrade load test environment to match production scale | Improve | [Name] | [YYYY-MM-DD] | ☐ Not started |
| Add "performance check" step to deploy checklist | Improve | [Name] | [YYYY-MM-DD] | ☐ Not started |
| Create runbook for "DB connection exhaustion" incidents | Improve | [Name] | [YYYY-MM-DD] | ☐ Not started |
> Review these action items in your next sprint planning or retro and close the loop.
---
## 9. Follow-up
- How will you verify that these actions were completed?
- Do any actions warrant a re‑review of this postmortem in the future?
- Where is this postmortem stored so future teams can find it? (e.g. HighFly project, docs repo)Want incident learnings that actually prevent repeat outages?
Use this template inside HighFly to link postmortem action items directly to sprint work, so fixes and improvements are tracked, prioritized, and actually shipped instead of forgotten in a doc.
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