m2ai-portfolio 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:
- What happened -- the observed bad behavior or output
- What was expected -- the correct behavior
- System context -- which agent/model/pipeline, what inputs triggered it
- 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):
| Pattern | Description | Diagnostic Signal |
|---|---|---|
| Context Degradation | Agent lost track of critical context mid-task due to window limits, compaction, or prompt structure | Output quality dropped partway through; early steps were fine |
| Specification Drift | The spec/prompt was ambiguous or incomplete; agent filled gaps with assumptions | Agent did something "reasonable" but wrong; spec didn't define the edge case |
| Sycophantic Confirmation | Agent agreed with flawed premises or user errors instead of pushing back | User provided wrong info; agent incorporated it without challenge |
| Tool Selection Error | Agent chose the wrong tool, wrong API, or wrong approach for the task | Right intent, wrong execution method; tool existed but wasn't selected |
| Cascade Failure | One error propagated through multiple steps, each compounding the damage | Small initial mistake; large final deviation; intermediate steps didn't catch it |
| Silent Failure | Agent completed without errors but output was wrong; no signal that anything failed | "Success" with bad results; no errors, no warnings, no escalation |
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:
- Why did the failure occur? -> [pattern-level answer]
- Why was that possible? -> [missing guardrail / spec gap]
- Why was that guardrail missing? -> [process / design gap]
- Continue until you hit a systemic cause
- 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.