Escalation Design
SkillMediaWhen and how AI should escalate to humans, refuse, or ask for clarification.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Escalation Design skill
What this skill tells your AI
The instructions your AI receives, as published by owl-listener/ai-design-skills in skills/ai-alignment-reasoning/escalation-design/SKILL.md and read by ahel’s review.
Escalation is what happens when the AI reaches the boundary of what it should handle alone. Designing escalation well means the user gets help instead of a dead end — and the AI knows its limits.
Escalation Triggers
The AI should escalate when:
- Confidence is low: The AI isn't sure its output is correct or helpful
- Stakes are high: The decision has significant consequences (financial, medical, legal, safety)
- Emotional distress: The user shows signs of crisis, distress, or vulnerability
- Ambiguity is unresolvable: The AI can't determine intent even after clarification
- Scope boundary: The request is outside what the AI is designed to handle
- Policy boundary: The request approaches or crosses a guardrail
- Conflict: The user disagrees with the AI and the disagreement can't be resolved
Escalation Types
- To human support: Transfer to a human agent with full context
- To the user themselves: "This decision is yours to make" — handing back agency
- To a specialist: Routing to domain-specific help (medical, legal, technical)
- To a supervisor/admin: Flagging for organisational review
- Self-escalation: The AI flags its own output for review before delivering it
Designing the Escalation Experience
The user's experience of escalation matters:
- Context transfer: When escalating to a human, pass the full conversation. Don't make the user repeat themselves.
- Warm handoff: "I'm connecting you with someone who can help with this" — not a cold redirect.
- Expectation setting: Tell the user what will happen next and how long it might take.
- Graceful degradation: If no human is available, offer alternatives — not a dead end.
- Dignity: Never make the user feel stupid for needing escalation.
Escalation Anti-Patterns
- The infinite loop: AI keeps trying instead of escalating, frustrating the user
- Premature escalation: AI escalates when it could easily handle the request, annoying the user
- Context loss: User has to start over after escalation
- Blame shifting: AI implies the user caused the problem
- Hidden escalation: Escalation happens without the user knowing
Design Artefacts
- Escalation trigger matrix: Trigger | Threshold | Escalation Type | User Experience
- Escalation flow diagrams per feature
- Context handoff specifications
- Fallback path designs for when escalation isn't available
- Escalation quality metrics
Signals
- GitHub stars
- 173
- Forks
- 33
- Last commit
- Jun 2026
Advanced
- Catalog kind
- skill
- Gateway key
escalation-design- Source
- github.com/owl-listener/ai-design-skills