human-in-the-loop

Agent Building

Designing intervention points where humans review, approve, or redirect agent work.

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Source SKILL.md: https://github.com/Owl-Listener/ai-design-skills/blob/HEAD/claude-plugin/design-agent-orchestration/skills/human-in-the-loop/SKILL.md

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Human-in-the-Loop

Human-in-the-loop design defines when, where, and how humans intervene in automated workflows. Too little human involvement and the system makes dangerous mistakes. Too much and you've just built an expensive notification system.

Intervention Types

Approval gates: The system pauses and waits for human approval before proceeding
Review checkpoints: The system presents results for human review but can continue if no objection
Correction opportunities: The system shows its work and the human can edit before it's finalised
Override controls: The human can stop, redirect, or undo the system's actions at any time
Monitoring dashboards: The human passively observes the system and intervenes only when needed

When to Require Human Intervention

High stakes: Actions that are expensive, irreversible, or affect many people
Low confidence: The system is uncertain about the right action
Novel situations: The input or context is outside the system's training distribution
Ethical judgments: Decisions that require moral reasoning or value trade-offs
Legal requirements: Regulatory or compliance requirements mandate human review
User request: The user explicitly asks for human involvement

Designing Intervention Points

For each intervention point:

Trigger: What causes the intervention? (confidence threshold, stakes level, user request, policy requirement)
Presentation: What does the human see? (summary, full context, recommendations, options)
Time constraint: How quickly must the human respond? What happens if they don't?
Decision options: What can the human do? (approve, reject, edit, escalate, defer)
Feedback integration: How does the human's decision feed back into the system?

Avoiding Human Bottlenecks

Human intervention is expensive and slow. Design to minimise unnecessary intervention:

Graduated autonomy: Start with more human oversight, reduce as the system proves reliable
Batch review: Group similar decisions for efficient human processing
Smart routing: Send interventions to the right human based on expertise and availability
Default actions: If the human doesn't respond within a time window, take a safe default action
Learning from interventions: Use human decisions to improve the system so it needs less intervention over time

Design Artefacts

Intervention point map (where in the workflow, what triggers each)
Intervention interface designs (what the human sees and can do)
Time constraint specifications
Graduated autonomy plans
Intervention analytics specifications (tracking how often, why, and what humans decide)