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Human-as-a-Tool (HaaT) UX: Interrupt & Resume, Overrides, SLAs

Introduction

Human-as-a-Tool (HaaT) UX treats expert reviewers as on-demand capabilities inside an AI product. Instead of making people the primary decision-makers (classic HITL), HaaT integrates humans as targeted tools that can be invoked, paused, resumed, or overridden with clear SLAs, controls, and audit trails. This page provides concrete patterns for interrupt/resume, queueing and routing, reviewer SLAs, override controls, and auditability—plus an implementation blueprint founders can ship.

Definitions and scope

  • HaaT vs. HITL

  • HaaT: People are invoked contextually (as a tool) to unblock or improve the AI’s work at specific steps. Useful for escalations, edge cases, and exception handling.

  • HITL: People continuously supervise or gate AI decisions. Best for high-risk or early-stage systems.

  • When to prefer HaaT

  • Your default path is automated, but quality or risk thresholds trigger targeted human help.

  • You need predictable latency and cost with bounded human involvement.

Note: See our HITL pattern and RAG Evaluation pattern for complementary approaches (cross-references; no external links provided here). For Zypsy’s broader product and engineering support, see Zypsy Capabilities.

Pattern 1 — Interrupt & Resume

Design the system so any long-running task can be paused, queued, and resumed without losing context.

  • Checkpointing

  • Persist minimal, replayable state at task boundaries: inputs, intermediate outputs, model/version, prompts, tools called, and a deterministic seed or idempotency key.

  • Emit human-readable timelines so reviewers can see what happened and when.

  • Interrupt semantics

  • Explicit pause reasons (risk threshold, confidence below X, PII detected, policy conflict, user request).

  • Safe-stop: halt further side effects; commit a checkpoint; surface next best action.

  • Resume semantics

  • Resume tokens scoped to a specific task version to avoid replaying stale context after model or policy updates.

  • Timeouts and backoff policies; if expired, escalate or re-run from last good checkpoint.

  • End-user UX

  • Clear status chips: “in review,” “waiting for information,” “scheduled.”

  • One-tap resume or “Undo/Cancel” where safe; show expected SLA and queue position.

Pattern 2 — Queueing and routing

Treat humans like specialized tools with skills, capacity, and SLAs.

  • Routing inputs

  • Skills and tags (domain, language, jurisdiction, safety category) matched to the task.

  • Data minimization: redact or mask PII; show only what’s required.

  • Capacity management

  • Per-queue concurrency caps, working hours, on-call rosters, and overflow policies (auto-defer or auto-accept).

  • Fairness: avoid “rich-get-richer” queues; apply round-robin or priority aging.

  • Backpressure

  • When queues breach thresholds, auto-tighten model thresholds, defer non-urgent tasks, or enable alternate flows.

Pattern 3 — Reviewer SLAs

Define response-time and quality targets with deterministic breach actions. Display SLAs in-product so users know what to expect. Use them to drive escalations and cost controls.

SLA tier First response target Resolution target Typical use Breach action
P0 (critical) ≤ 5 min ≤ 30 min Safety, finance, legal blocks Auto-escalate to senior reviewer and notify owner; if not accepted, fail-safe block
P1 (high) ≤ 30 min ≤ 4 hrs Customer-visible errors, VIP issues Escalate to on-call; if no pickup, convert to P0
P2 (standard) ≤ 4 hrs ≤ 24 hrs Quality checks, editorial Re-route to overflow pool; extend ETA to user
P3 (deferred) ≤ 24 hrs ≤ 3 days Low-risk backlog Auto-close if stale; allow user re-open

Implementation notes

  • Track SLA per tenant and per queue; allow contractual overrides.

  • Expose “ETA to human review” and “position in queue” in UI; update in real time.

Pattern 4 — Override controls (“break-glass”)

Give authorized users the ability to overrule the AI, but make it safe, narrow, and auditable.

  • Role-based access control (RBAC) with dual-approval for irreversible actions.

  • Force proceed, force block, or edit output with reason codes and evidence attachments.

  • Time-bound overrides with auto-expiration and post-hoc review.

  • Cooldowns for repeated overrides to the same rule/model.

Pattern 5 — Auditability and forensics

Design for post-incident clarity and continuous improvement.

  • Immutable logs: inputs, outputs, prompts, tools, models/versions, temperatures/seeds, reviewers, decisions, timestamps, and environment hashes.

  • Chain-of-custody for artifacts; content hashes for diffs across edits and resumes.

  • Data minimization and retention windows by data class; redact at source when possible.

  • Reviewer performance analytics (agreement rates, correction impact) for coaching—not just enforcement.

For examples of rigor around AI safety and governance at enterprise scale, see Zypsy’s work with Robust Intelligence. For complex AI product UX, see Captions.

Operational data model (minimal set)

  • Task: id, tenant, type, priority, state, SLA tier.

  • Checkpoint: task_id, step, input/output hashes, model_config, tool calls.

  • Queue: skills, capacity, latency, on-call roster.

  • Reviewer: id, roles, skills, performance metrics, schedule.

  • OverrideRecord: actor, action, reason_code, evidence, expiration.

  • AuditLog: normalized event stream with tamper-evident storage.

Reviewer console UX (key surfaces)

  • Queue list with filters (priority, aging, skills) and breach flags.

  • Timeline panel showing model steps, checkpoints, and diffs.

  • Redaction view: toggle masked/unmasked fields (permission-gated).

  • Decision composer: approve/deny/edit with reason codes and templates.

  • SLA/ETA widget and escalation actions.

End-user UX guidelines

  • Explain why a task was paused; show ETA and what will happen next.

  • Provide a safe “cancel” with clear consequences.

  • On resume, summarize what changed since the pause.

Metrics that matter

  • Latency: time-to-first-human, time-to-resolution, percent within SLA.

  • Quality: human–AI disagreement rate, post-override defect rate, reviewer agreement (pair-audit), model drift detected via override trends.

  • Cost: human minutes per task, cost per resolved exception, rework rate.

  • Experience: user satisfaction for paused flows; abandonment rate during review.

Failure and edge cases to handle

  • Model or policy update mid-review: auto-invalidate stale resume tokens; re-run from last checkpoint.

  • Multi-tenant isolation: ensure no cross-tenant leakage in queues or logs.

  • User edits inputs while paused: branch and require reviewer re-ack.

  • Long-tail inactivity: auto-close with archived state; allow reopen with new SLA.

  • Privacy: ensure masked views by default; unmask only with elevated permission and audit.

Implementation blueprint

  • Orchestration

  • Use an event-driven pipeline where each step emits normalized audit events and creates checkpoints.

  • Idempotent step handlers keyed by task+step to support retries and resume.

  • Policy engine

  • Declarative rules trigger interrupts, route to queues, and set SLA tiers.

  • Storage

  • Separate PII from task metadata; store hashes in audit logs; encrypt sensitive blobs.

  • Governance

  • Weekly override review; calibrate reason codes; retire unused break-glass paths.

How Zypsy helps founders ship HaaT patterns

  • Product design and research: Reviewer console UX, end-user pause messaging, and decision reason taxonomies. See Zypsy Capabilities.

  • Engineering enablement: Event schemas, checkpoint storage, RBAC, and orchestration. See Zypsy Capabilities.

  • Venture support: If you’re building AI-driven products, Zypsy can pair design with investment via Zypsy Capital or services-for-equity via Design Capital.

Related reading on transparency and reliability

Checklist (ship-ready)

  • [ ] Interrupt reasons and safe-stop checkpoints implemented

  • [ ] Resume tokens versioned and time-bound

  • [ ] Queues defined with skills, capacity, and breach thresholds

  • [ ] SLA tiers surfaced in UI with deterministic breach actions

  • [ ] Override controls gated by RBAC with reason codes and dual control

  • [ ] Immutable, privacy-aware audit logs with content hashes

  • [ ] Metrics dashboard: latency, quality, cost, experience

  • [ ] Weekly override and SLA breach review

Ready to implement HaaT in your product? Contact us at Zypsy → Contact or explore more founder content on Zypsy Insights.