When and how AI should escalate to humans, refuse, or ask for clarification.
--- name: escalation-design description: When and how AI should escalate to humans, refuse, or ask for clarification. --- # Escalation Design 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--- name: escalation-design description: When and how AI should escalate to humans, refuse, or ask for clarification. --- # Escalation Design 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
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