escalation-design

Agent Building

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

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Source SKILL.md: https://github.com/Owl-Listener/ai-design-skills/blob/HEAD/claude-plugin/ai-alignment-reasoning/skills/escalation-design/SKILL.md

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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