Large People ModelHuman Operating Architecture

Template & Working Tool · LPM Knowledge Object

Human-in-the-Loop Model

A model for defining where humans review, approve, override, monitor, or stop AI-assisted work.

Modelv1.0.0GovernanceAI Amplification

Problem it solves

Governance is either too slow to support execution or too weak to manage risk.

Who should use it

Operating-model, product, technology, and transformation leaders

Estimated time

30–45 minutes for a first working session

Three-Step Quick Start

1Map the AI-assisted workflow.
2Define review, approval, override, and monitoring points.
3Assign owners and evidence for each human control.

Open the public PDF

The PDF action is direct and public. All available packaged formats are also public and require no registration.

Object Overview

What this object is

Human-in-the-Loop Model is a reusable LPM knowledge object that helps organizations clarify the human control points required to use AI safely in operational workflows. It gives teams a structured way to make governance visible, owned, and reviewable.

Why it matters

As companies scale AI, weak operating-model structures become amplified. This object helps prevent ai scales faster than oversight, auditability, and risk ownership. by defining role, authority, and operating-model boundaries.

Layer Alignment

Where it fits in LPM

Primary LPM layer

Governance Architecture

Defines the controls, policies, review loops, and decision boundaries that keep execution safe without slowing it unnecessarily.

Supporting layers

AI Amplification

Why it belongs here

This object sits in Governance because it turns governance into a concrete artifact with owners, evidence, review cadence, and action paths.

Weakness it exposes

AI scales faster than oversight, auditability, and risk ownership.

Usage

How to use it

1Select the business area, workflow, platform, or AI initiative being assessed.
2Identify the accountable owner and required participants.
3Complete the working DOCX version with the team.
4Use the PDF as the reference guide.
5Capture decisions, gaps, risks, and owners.
6Convert outputs into backlog items, governance actions, or Lapemo onboarding inputs.
7Review on the recommended cadence: Before launch and after incidents.

File Formats

Which file should you use?

PDF

Executive/reference version

Best for education, pre-read, sharing, and workshops.

DOCX

Editable working artifact

Best for facilitation, implementation, and client or internal completion.

Markdown

Website/source version

Best for publishing, documentation, and content reuse.

JSON

Structured knowledge object schema

Best for future Lapemo ingestion, scoring, validation, prompts, and workflows.

Outputs

What the organization should expect

Clearer ownership

Better decision traceability

Reduced ambiguity

Evidence-backed conversations

Better AI readiness

Better handoff into Lapemo later

Human review model

Control point map

AI escalation actions

Advanced specification, company-size variants, and future product notes

Company Scale

How this changes by company size

500+ employees

Use this to create baseline clarity.

Focus on named owners, simple governance, and reducing informal workarounds.

Included in this object.

5,000+ employees

Use this to standardize across functions and platforms.

Focus on cross-functional ownership, decision rights, evidence, and repeatability.

Included in this object.

10,000+ employees

Use this to create enterprise control and reviewability.

Focus on federation, risk tiers, governance bodies, AI boundaries, and auditability.

Included in this object.

Artifact Content

Source artifact

The full artifact content below is rendered from the Markdown source packaged with Human-in-the-Loop Model.

Reusable LPM Knowledge Object for the AI Amplification layer

Use this Human-in-the-Loop Model to make human review an operating design, not a vague safety phrase. The model defines where humans belong in AI-enabled work: before the work starts, while AI is producing recommendations, before actions are taken, after actions are logged, and when exceptions or drift appear. It helps companies scale AI without pretending that a human is accountable simply because someone was nearby, copied on an email, or able to theoretically intervene.

Core principles

Principle	Meaning
Human-in-the-loop is not enough by default	A loop is only useful when the human has authority, context, evidence, time, and the ability to change or stop the outcome.
Accountability stays with humans	AI may recommend, draft, summarize, classify, route, or act, but a named human or governance forum owns the outcome.
Review depth must match impact	Low-impact tasks can use lightweight review. High-impact decisions, system actions, regulated workflows, and external communications require stronger controls.
Review must happen at the right point	A human review after a bad decision has already reached a customer, employee, regulator, or system of record is not meaningful control.
Evidence must be reviewable	Reviewers need source lineage, confidence, assumptions, model output, tool calls, exceptions, and downstream impact, not polished AI summaries alone.
The human must be empowered	A reviewer without decision rights, escalation authority, or stop rights creates false assurance.
Over-review creates theater	Putting humans everywhere creates bottlenecks and rubber-stamping. The model should place humans where judgment, authority, ethics, risk, or accountability matters.
Loops must be monitored	Override rates, review quality, delays, escalations, incidents, drift, and audit findings determine whether the loop is working.

Loop types

Loop type	Description
Human-in-the-front	A human defines intent, scope, policy, data boundary, prompt pattern, and approval rule before AI begins work.
Human-in-the-decision	AI prepares options or recommendations, but a human decision owner makes the decision.
Human-in-the-approval	AI drafts or prepares an action, but a human approves before execution, external communication, or system-of-record change.
Human-on-the-loop	AI operates within approved boundaries while humans monitor signals, exceptions, drift, quality, and risk.
Human-over-the-loop	A human or governance forum can pause, restrict, override, roll back, or retire the AI system.
Human-after-the-loop	A human audits samples, logs, outcomes, incidents, overrides, and control evidence after execution.

Required fields

Field	Definition	Required
Loop ID	Unique identifier for the human review or intervention point	Yes
AI use case or agent	Initiative, workflow, model, automation, assistant, or agent the loop applies to	Yes
Business outcome	Outcome, decision, workflow, service, control, or risk the AI supports	Yes
Impact tier	Low, moderate, high, critical, regulated, customer-facing, employee-impacting, financial, security, privacy, or control-impacting	Yes
Loop type	Human-in-the-front, human-in-the-decision, human-in-the-approval, human-on-the-loop, human-over-the-loop, or human-after-the-loop	Yes
Review trigger	Event, decision, threshold, exception, confidence score, risk condition, or workflow step that requires human involvement	Yes
Human reviewer role	Named person, role, queue, forum, control owner, decision owner, business owner, or accountable approver	Yes
Reviewer authority	Approve, reject, revise, escalate, pause, override, roll back, accept risk, or retire	Yes
Evidence required	Sources, lineage, assumptions, output, confidence, tool calls, logs, risk tier, policies, prior decisions, and control evidence reviewed by the human	Yes
Allowed AI action before review	What AI may do before a human is involved	Yes
Blocked AI action before review	What AI must not do until human review or approval happens	Yes
Decision rights link	Decision rights model, matrix, owner, or forum tied to the loop	Required for decisions
Control link	Control, monitoring signal, approval gate, logging rule, or audit requirement tied to the loop	Required for moderate and above
Escalation path	Where the reviewer sends uncertainty, conflict, failed evidence, policy exception, or unsafe output	Yes
SLA or review window	Required response time for the loop to be useful	Required for operational workflows
Override rule	When a human may override AI and what must be logged	Yes
Stop or pause rule	Who can stop the AI process and under what conditions	Required for high and above
Evidence retention rule	What review evidence is retained, where it lives, and how long it remains valid	Yes
Monitoring signals	Review backlog, approval rate, rejection rate, override rate, escalations, drift, incidents, and value impact	Yes
Review cadence	Weekly, monthly, quarterly, release-based, incident-based, source-change based, model-change based, or policy-change based	Yes

Model components

Component	Description
Trigger	The condition that activates human involvement, such as low confidence, high risk, external communication, system update, policy exception, or material decision.
Reviewer	The human role with enough context and authority to judge the AI output or action.
Evidence package	The source material, lineage, assumptions, prior decisions, policies, confidence, logs, and generated output available for review.
Authority boundary	What the reviewer can approve, reject, revise, escalate, pause, override, or retire.
System action rule	What AI can and cannot do before human approval, especially for write access, external communication, commitments, or control-impacting actions.
Escalation route	The route when the reviewer cannot approve, evidence is weak, authority is unclear, or risk exceeds tolerance.
Audit trail	The log of what AI proposed, what evidence was used, who reviewed, what decision was made, and what changed downstream.
Feedback loop	How review outcomes improve prompts, sources, controls, training, workflow design, access, and governance rules.

Human-in-the-loop checklist

1. Use case and impact classification

Checklist item	Status	Guidance
AI use case or agent is named	Yes / No / Partial	The loop must attach to a real AI capability, not a broad program label.
Business outcome is defined	Yes / No / Partial	Review design should match the outcome AI is affecting.
Impact tier is assigned	Yes / No / Partial	Classify by the highest impact the AI can create, not the average task.
Regulatory, customer, employee, financial, privacy, security, and control impacts are identified	Yes / No / Partial	Higher-impact categories require stronger human authority and evidence.

2. Loop placement and trigger design

Checklist item	Status	Guidance
Loop type is selected	Yes / No / Partial	Front, decision, approval, on, over, or after the loop.
Review trigger is explicit	Yes / No / Partial	Define the exact condition that requires human review.
Human review happens before irreversible or external action	Yes / No / Partial	Approval after harm occurs is not a control.
Escalation trigger is documented	Yes / No / Partial	Define when the reviewer must escalate rather than decide.

3. Human authority and accountability

Checklist item	Status	Guidance
Reviewer role is named	Yes / No / Partial	A generic human reviewer is not enough.
Reviewer has decision rights	Yes / No / Partial	The reviewer must have authority to approve, reject, revise, escalate, or stop.
Accountable business owner is named	Yes / No / Partial	The reviewer may not always be the accountable owner, but ownership must be clear.
Control owner is named for higher-risk loops	Yes / No / Partial	Human review must connect to controls and evidence, not just personal judgment.

4. Evidence and context requirements

Checklist item	Status	Guidance
Evidence package is defined	Yes / No / Partial	Sources, assumptions, confidence, lineage, prior decisions, policies, and output must be reviewable.
AI summary is not treated as sole evidence	Yes / No / Partial	Reviewers need access to underlying source material when impact requires it.
Freshness and source-of-truth rules are defined	Yes / No / Partial	Human review is weak if the evidence is stale or unofficial.
Evidence retention rule is defined	Yes / No / Partial	Log enough to replay why the human approved, rejected, revised, or escalated.

5. AI action boundary

Checklist item	Status	Guidance
Allowed AI actions before review are listed	Yes / No / Partial	Define whether AI may retrieve, summarize, draft, classify, score, route, update, trigger, or communicate.
Blocked AI actions before review are listed	Yes / No / Partial	Block external commitments, regulated decisions, control bypass, system updates, and sensitive communication when needed.
Write access requires approval rule	Yes / No / Partial	Any system-of-record update must have explicit approval or approved automation boundary.
Customer or employee-impacting actions require stronger review	Yes / No / Partial	Human review must occur before high-impact communications or outcomes.

6. Controls, monitoring, and auditability

Checklist item	Status	Guidance
Control link is documented	Yes / No / Partial	Tie the loop to preventive, detective, corrective, access, or audit controls.
Monitoring signals are defined	Yes / No / Partial	Track override rate, rejection rate, escalation rate, backlog, incidents, drift, and quality.
Stop, pause, rollback, or override path is documented	Yes / No / Partial	The human must be able to intervene when AI behavior becomes unsafe or unreliable.
Review quality is assessed	Yes / No / Partial	A rubber-stamp loop is worse than no loop because it creates false confidence.

7. Lifecycle and improvement

Checklist item	Status	Guidance
Loop review cadence is defined	Yes / No / Partial	Review design must change as AI, data, policy, tools, and workflows change.
Review outcomes feed improvement	Yes / No / Partial	Use human decisions to improve prompts, sources, controls, training, and routing.
Retirement rule is defined	Yes / No / Partial	Remove loops that become unnecessary, ineffective, slow, or automated with sufficient controls.
Supersession rule is defined	Yes / No / Partial	When a new model, source, workflow, control, or policy changes the loop, supersede the old version.

Scale versions

Version for 500+ employee company

Dimension	Recommended pattern
Design intent	Create lightweight human review rules before AI tools move from assistant use to workflow, communication, decision, or system-action use.
Minimum scope	Document owner, use case, impact tier, trigger, reviewer, evidence, allowed actions, blocked actions, escalation, and review cadence.
Operating pattern	Small AI governance group or transformation owner defines common loop patterns and reviews higher-risk use cases.
AI focus	Prevent teams from relying on informal review, chat-based approvals, or unlogged human judgment.
Governance need	Connect to AI governance checklist, agent accountability checklist, decision rights model, evidence checklist, and control map.
Red flags	A human is said to be in the loop but no one knows what they review, what evidence they see, or what authority they have.

Version for 5,000+ employee company

Dimension	Recommended pattern
Design intent	Create repeatable loop patterns across business units, data domains, platforms, workflows, and governance forums.
Minimum scope	Add role-based review tiers, evidence packages, approval SLAs, escalation routing, control links, logs, and monitoring signals.
Operating pattern	Business units apply standard loop models while enterprise governance reviews high-impact, regulated, customer-facing, employee-impacting, or system-action AI.
AI focus	Prevent inconsistent human review rules across teams, duplicated approvals, weak evidence, and review bottlenecks.
Governance need	Connect to source-of-truth map, data lineage map, integration map, workflow inventory, risk acceptance register, and decision log.
Red flags	Reviewers are overloaded, approvals are rubber-stamped, or AI can act faster than humans can govern exceptions.

Version for 10,000+ employee company

Dimension	Recommended pattern
Design intent	Create enterprise-grade human accountability across regions, regulated functions, shared platforms, vendors, agents, and multi-agent chains.
Minimum scope	Full traceability across use case, owner, decision, workflow, evidence, sources, tools, controls, risk, approvals, incidents, and lifecycle state.
Operating pattern	Central AI governance defines standards and risk tiers while federated owners manage loop execution, evidence, escalation, monitoring, and improvement.
AI focus	Prioritize high-risk loops for system-action agents, external communication, regulated decisions, model-driven prioritization, employee-impacting outcomes, and customer-impacting workflows.
Governance need	Integrate with GRC, IAM, model risk, privacy, legal, security, data governance, enterprise architecture, internal audit, and executive control-plane reporting.
Red flags	A chain of agents and humans makes it unclear who reviewed, who approved, what evidence was used, and who owned the outcome.

Scoring logic

Dimension	Score	What good looks like
Impact classification	0-5	AI use case is classified by strongest possible business, customer, employee, financial, regulatory, security, privacy, and control impact.
Loop placement	0-5	Human involvement occurs at the right point before material decisions, system actions, external communication, or irreversible outcomes.
Reviewer authority	0-5	The reviewer has clear authority to approve, reject, revise, escalate, pause, override, or stop.
Ownership clarity	0-5	Business owner, reviewer, decision owner, technical owner, and control owner are clear where needed.
Evidence quality	0-5	Reviewer receives source lineage, freshness, confidence, assumptions, policy, prior decisions, logs, and AI output appropriate to impact.
Action boundary	0-5	AI allowed and blocked actions before review are explicit, especially for write access, commitments, and external communication.
Control coverage	0-5	Loop is tied to controls, approvals, logs, monitoring, exception handling, and auditability.
Escalation readiness	0-5	Uncertainty, policy exceptions, low confidence, high risk, and failed evidence have a defined escalation path.
Monitoring discipline	0-5	Backlog, delays, approval rate, rejection rate, override rate, incidents, drift, review quality, and value impact are monitored.
Lifecycle governance	0-5	Loop has review cadence, change triggers, retirement rule, supersession rule, and improvement path.

Suggested readiness score: average the ten scores, then classify:

0-1.9: Loop theater / unmanaged human review
2.0-3.4: Documented but weak intervention model
3.5-4.4: Governed human-in-the-loop model
4.5-5.0: Enterprise-ready human accountability model

AI prompts

Classify this AI use case by impact tier, strongest possible action, review trigger, reviewer authority, evidence needs, control needs, and escalation route.
Review this human-in-the-loop design and identify missing owners, weak triggers, unclear reviewer authority, insufficient evidence, missing controls, and rubber-stamp risk.
Given this AI workflow, propose the correct loop type: human-in-the-front, human-in-the-decision, human-in-the-approval, human-on-the-loop, human-over-the-loop, or human-after-the-loop.
Generate allowed AI actions before review, blocked AI actions before review, human approval rules, escalation triggers, evidence package, and monitoring signals.
Score this loop from 0 to 5 across impact classification, loop placement, reviewer authority, ownership clarity, evidence quality, action boundary, control coverage, escalation readiness, monitoring discipline, and lifecycle governance.
Convert this human-in-the-loop model into Lapemo objects for AI initiative, agent, owner, reviewer, decision, source, workflow, control, evidence, risk, escalation, monitoring signal, and review cadence.

Validation rules

Every material AI use case must have an explicit human accountability model before production use.
Every loop must define trigger, reviewer role, reviewer authority, evidence package, allowed AI actions, blocked AI actions, escalation path, and monitoring signal.
A named accountable business owner must exist even when review is performed by a queue, forum, control owner, or delegate.
High-impact, critical, regulated, customer-facing, employee-impacting, financial, security, privacy, or control-impacting AI must have human approval or governance-approved automation boundaries before material action.
Human review may not rely only on AI-generated summary when the impact tier requires source evidence or lineage.
Reviewers must have authority to approve, reject, revise, escalate, pause, override, roll back, or stop according to risk tier.
System-of-record updates, external commitments, regulated decisions, control exceptions, and sensitive communications require explicit approval rules.
Every loop above low impact must link to at least one control, log, evidence requirement, and escalation path.
Rubber-stamp loops must be redesigned, automated with stronger controls, or removed.
Loop design must be reviewed when sources, model, vendor, workflow, policy, controls, ownership, impact tier, or system access changes.

Lapemo ingestion mapping

Lapemo object	Fields / entities	Use
AI Initiative	initiative_id, name, purpose, impact_tier, lifecycle_status	Connects loop to active AI work.
Agent	agent_id, agent_type, allowed_actions, blocked_actions, tool_access	Defines whether loop applies to an agent or agent chain.
Owner	business_owner, reviewer_role, technical_owner, control_owner, decision_owner	Preserves accountability across humans and systems.
Decision	decision_type, decision_rights_link, approval_rule, supersession_rule	Connects AI output to decision authority.
Workflow	workflow_id, trigger, handoff, SLA, system_action_rule	Places the loop inside real operating flow.
Evidence	source_lineage, confidence, assumptions, logs, retained_evidence	Defines what the human reviews and what is stored.
Control	control_id, control_type, monitoring_signal, exception_rule, audit_rule	Connects loop to governance control.
Risk	risk_tier, risk_acceptance_link, escalation_path, residual_risk	Determines governance route and acceptance needs.
Monitoring Signal	approval_rate, rejection_rate, override_rate, backlog, incidents, drift	Measures whether the loop is working.
Knowledge Object	version, owner, review_cadence, last_reviewed, superseded_by	Keeps the model reusable and current.

Reusable knowledge object structure

Layer	Reusable asset
Human guide	Plain-language explanation of where humans belong in AI-enabled work.
Template	Downloadable DOCX/PDF/Markdown model for workshops and governance reviews.
Machine schema	JSON structure for ingestion into Lapemo and future automation.
Guided skill	AI-assisted workflow that classifies AI use cases, recommends loop placement, scores readiness, and routes governance.

Website positioning

Use this artifact as a downloadable AI Amplification model and as a future guided skill inside Lapemo. The website version should make a strong claim: human-in-the-loop is not a checkbox. It is the operating model that determines when human judgment, authority, evidence, control, and accountability are required before AI work becomes an enterprise outcome.

Future Lapemo Use

The JSON schema turns human-in-the-loop model into software.

Lapemo can use this knowledge object as a guided workflow, scoring model, evidence record, governance input, and operating intelligence object. The schema is public for inspection and evaluation; production ingestion and governed execution remain separate product capabilities.