Failure Post-Mortem Builder

Build a structured post-mortem for an AI system failure. Walks through diagnosis using 6 named failure patterns and produces a publishable incident report.

Trigger

Use when the user says "post-mortem", "failure analysis", "what went wrong with the agent", "debug this failure", or describes an AI system that produced bad output.

Phase 1: Incident Capture

Ask the user for:

1. What happened -- the observed bad behavior or output
2. What was expected -- the correct behavior
3. System context -- which agent/model/pipeline, what inputs triggered it
4. Impact -- who was affected, what was the blast radius

Keep questions tight. One round of clarification max.

Phase 2: Failure Pattern Classification

Classify the failure against these 6 patterns (multiple can apply):

For each matching pattern, explain:

• Why this pattern fits
• Which specific moment in the execution triggered it
• What guardrail was missing

Phase 3: Root Cause

Identify the deepest cause. Use the "5 Whys" technique:

1. Why did the failure occur? -> [pattern-level answer]
2. Why was that possible? -> [missing guardrail / spec gap]
3. Why was that guardrail missing? -> [process / design gap]
4. Continue until you hit a systemic cause
5. State the root cause in one sentence

Phase 4: Post-Mortem Report

Generate a structured report:

# AI Failure Post-Mortem: [Short Title]

**Date**: [date]
**System**: [agent/pipeline name]
**Severity**: [LOW / MEDIUM / HIGH / CRITICAL]
**Status**: [investigating / mitigated / resolved]

## Incident Summary
[2-3 sentences: what happened, what was expected, what was the impact]

## Timeline
- [timestamp/step] -- [what happened]
- ...

## Failure Patterns Identified
### [Pattern Name]
[explanation, evidence, missing guardrail]

## Root Cause
[one-sentence root cause from 5 Whys]

## Corrective Actions
| Action | Type | Priority | Owner |
|--------|------|----------|-------|
| [specific fix] | [prevent / detect / mitigate] | [P0-P3] | [who] |

## Lessons Learned
- [insight that applies beyond this specific incident]

Phase 5: Verification

• Confirm the report captures the user's understanding of what happened
• Ask if any corrective actions should be implemented now
• Offer to save the report to the vault or a project directory

Source

Extracted from Nate Kadlac newsletter (2026-03-26) -- "The K-Shaped AI Labor Market" -- failure pattern taxonomy for AI system quality judgment.