escalation-governance

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Assess whether to escalate models. Use when evaluating reasoning depth.

AI & Automation 339 stars 35 forks Updated 2 days ago MIT

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# Escalation Governance ## Overview Model escalation (haiku→sonnet→opus) trades speed/cost for reasoning capability. This trade-off must be justified. **Core principle:** Escalation is for tasks that genuinely require deeper reasoning, not for "maybe a smarter model will figure it out." ## The Iron Law ``` NO ESCALATION WITHOUT INVESTIGATION FIRST ``` **Verification:** Run the command with `--help` flag to verify availability. Escalation is never a shortcut. If you haven't understood why the current model is insufficient, escalation is premature. ## When to Escalate **Legitimate escalation triggers:** | Trigger | Description | Example | |---------|-------------|---------| | Genuine complexity | Task inherently requires nuanced judgment | Security policy trade-offs | | Reasoning depth | Multiple inference steps with uncertainty | Architecture decisions | | Novel patterns | No existing patterns apply | First-of-kind implementation | | High stakes | Error cost justifies capability investment | Production deployment | | Ambiguity resolution | Multiple valid interpretations need weighing | Spec clarification | ## When NOT to Escalate **Illegitimate escalation triggers:** | Anti-Pattern | Why It's Wrong | What to Do Instead | |--------------|----------------|---------------------| | "Maybe smarter model will figure it out" | This is thrashing | Investigate root cause | | Multiple failed attempts | Suggests wrong approach, not insufficient capability | Question your assu...

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Author
athola
Repository
athola/claude-night-market
Created
10 months ago
Last Updated
2 days ago
Language
Python
License
MIT

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