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escalation-design

When and how AI should escalate to humans, refuse, or ask for clarification.

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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
Repository
Owl-Listener/ai-design-skills
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