Readiness layer
Public-safe planning content. Readiness templates, Advisor outputs, and Blueprint drafts help teams prepare review conversations. They do not approve production use.
Governed AI Worker Operating Layer
Govern AI workers before they act with zero trust, governance, ownership, human oversight, evidence, accountability, control, and runtime governance built into every handoff.
Scaled Agents™ helps organizations govern AI workers before they act across tools, data, systems, and workflows. Start with a draft Blueprint, then map ownership, purpose, scope, permissions, risk tier, controls, approval path, evidence, lifecycle state, and next steps before implementation or production decisions.
Operating Model
How Scaled Agents Works
Scaled Agents connects AI worker intake, ownership, policy review, runtime gates, execution evidence, and lifecycle visibility into one governed operating model. Teams can see who owns the worker, what it may do, where review is needed, when work should pause or escalate, and which evidence supports the current status.
The Agent Passport defines identity, owner, approved scope, and review posture. Runtime authorization applies those boundaries at action time before higher-risk work proceeds, while replay-safe evidence helps preserve review context and decision history.
The post-launch question is operational accountability: who owns the AI worker after launch, who can change or pause its authority, how exceptions are reviewed, and how value, cost, quality, and control evidence stay current before wider scale.
The release-candidate story is simple: Passport sets the standing authority, runtime authorization checks the specific request, human review handles higher-risk or unclear movement, and execution receipts preserve what was reviewed, blocked, allowed, or escalated.
For a step-by-step view, see how a governed workflow moves from intake to reviewed next steps.
Explore the model
Overview covers the full image. Tabs 1-3 explain the numbered image areas. Advisor is the next step.
Public view
Overview
Scaled Agents gives teams a governed operating model for AI workers: who owns them, what they may do, what needs review, and what evidence supports the current status.
Named ownershipEvery AI worker should have a visible owner, purpose, scope, and lifecycle state before teams rely on it.
Review before relianceHigher-risk work can pause, route, or escalate through approval paths and evidence checks before consequential use.
Lifecycle visibilityMonitoring, evidence refresh, ownership changes, remediation, suspension, revocation, and retirement stay visible.
Review boundaryThis section explains the operating model. It does not represent legal advice, a compliance conclusion, security authorization, or approval for production use.
Foundation and services
What Scaled Agents Provides
Step 1 in the image separates what Scaled Agents provides into Products and Services. Products create the reusable governance records; services help teams design, set up, and roll out the operating model.
Products
- Agent Passport captures AI worker identity, purpose, owner, risk, systems, scope, and review posture.
- Agent Registry keeps inventory, status, ownership, and lifecycle visibility organized.
- Stamps record reviewed events, evidence updates, review milestones, and lifecycle signals.
- Tolls define control gates where evidence, approval, or escalation may be required.
Services
- Strategy and intake clarifies the AI worker opportunity, business outcome, and review boundary.
- Operating model design maps workflows, owners, reviewers, and control checkpoints.
- Governance setup defines review paths, evidence expectations, and escalation rules.
- Workflow and agent design shapes the governed work pattern before launch planning.
- Enablement and rollout prepares teams to operate the model with human accountability.
Image messageA structured foundation for enterprise agents starts with visible records, repeatable services, and clear ownership before scaling AI worker use.
Connected workflow
How They Work Together
Step 2 in the image shows the operating sequence from intake through improvement. Each row creates or updates governance context so the AI worker is visible before it moves closer to use.
01Use case intakeRequest and business context
02Agent Passport createdIdentity, purpose, owner, risk, systems
03Agent Registry updatedInventory, status, ownership
04Review evidenceSecurity, data, legal, business approval
05Control gates appliedDesign, access, launch, monitoring, renewal
06Launch and monitoringUsage, cost, performance, oversight
07Feedback and improvementUpdates, retraining, lifecycle decisions
DiscoverStart with the use case and business context before defining a governed AI worker.
Design, register, review, gateCreate the Passport, update the Registry, review evidence, and apply Tolls before higher-risk movement.
Launch, monitor, improve, renew or retireKeep usage, cost, performance, oversight, updates, retraining, and lifecycle decisions visible.
Business operating layer
How It Operates in the Business
Step 3 in the image shows the business operating model: a centralized foundation connected to human-governed agent operations, then applied close to the work across business functions.
Centralized foundationIdentity, governance, security, data boundaries, standards, and auditability create the common operating base.
Human-governed agent operationsOwners, approvers, reviewers, and domain teams keep AI worker activity accountable to the business context.
Procurementclose to vendor and tool decisions
Financeclose to cost and value context
Operationsclose to process execution
Legalclose to review boundaries
Customer Serviceclose to service handoffs
Riskclose to controls and escalation
Image messageCentralize the foundation. Scale the agents. Keep humans in control.
Advisor entry point
Readiness Advisor Overview
Use the Readiness Advisor to understand where an AI worker idea fits, what review may be needed, and what next step is appropriate before relying on a draft blueprint or recommendation.
Shape the ideaStart from a plain-language AI worker concept and clarify purpose, audience, workflow, ownership, and trust boundaries.
Surface review needsIdentify likely risk, data, approval, evidence, monitoring, and escalation considerations for human review.
Choose the next stepMove toward a guided intake, self-service form, readiness conversation, or deeper advisor page.
Public-safe useThe Advisor is a planning aid. It can help prepare intake context and next-step recommendations, but it does not approve an AI worker, authorize production use, or replace required human review.
Readiness-focused. Human-reviewed. Designed to support accountable AI worker operations without claiming legal, security, compliance, audit, or production use.
Stack Position
Where Scaled Agents sits in the AI governance stack
Scaled Agents sits in the AI worker governance control-plane layer: between standing authority and operational action. It helps teams connect Passport scope, Toll Gates, evidence, policy version, human review, runtime authorization, and lifecycle state before higher-risk AI-supported work moves forward.
Governance readiness explains what should be true. Observability explains what happened. Execution governance decides whether the proposed action may become operationally real now.
That control-plane layer becomes especially important after pilot: operating records should show the owner, authority boundary, cost owner, review cadence, escalation path, pause path, lifecycle state, and evidence needed to keep AI-supported work accountable.
Identity and authority layer
Implemented in local/mock records. Passport, Registry, Human Review, Toll Gate, Evidence Record, and Audit Export fixtures make ownership, scope, review posture, and missing inputs visible.
Action-time layer
Preview concept. Runtime Permit, Action Broker, commit-time recheck, execution receipt, and refusal records describe the action path; production enforcement requires approved implementation and customer integration.
Customer environment
Requires customer integration. Enterprise IAM, SIEM, cloud runtime, model hosting, connector execution, system-of-record writes, and operational monitoring remain customer-controlled or separately integrated systems.
Capability boundary matrix
Use these labels to separate public-safe planning, local MVP records, preview concepts, customer integration needs, and intentionally not-provided capabilities.
Provides
Implemented in local/mock records. Passport, Registry, Toll Gate Decision, Evidence Record, Human Review Item, Audit Export Package, and default-deny fixture validation.
Supports
Public-safe planning content. Governance readiness, evidence preparation, human-review planning, shared-responsibility mapping, lifecycle visibility, and training paths.
Plans
Preview concept. Runtime Permit, Action Broker, execution receipt, structural refusal, commit-time admissibility, policy versioning, and governed handoff continuity.
Integrates later
Requires customer integration. Customer identity, tool/API authorization, logs, orchestration, model/provider runtime, connector execution, deployment controls, and monitoring systems.
Does not replace
Not provided. Cloud runtime or model hosting, Enterprise IAM or SIEM replacement, Legal, compliance, audit, or security approval, Live production enforcement, customer operations, or final business decisions.
Public boundary
Not provided. Public materials do not claim certification, legal approval, compliance approval, security authorization, audit opinion, production authorization, or guaranteed outcomes.
Capability status boundaryRuntime authorization and execution-governance terms describe the intended control path. Public pages should treat production enforcement, live connector execution, customer data storage, and regulated approval as unavailable unless implementation evidence, tests, customer authorization, and release approval exist.
Operating Accountability
The real AI gap starts after launch.
Architecture and pilot planning are only the beginning. Scaled Agents helps teams prepare the operating record for what happens after an AI worker is used in real workflows: ownership, authority, evidence, exception handling, lifecycle state, and cost/value review.
Owner and mandate
Identify the business owner, technical owner, cost owner, reviewer, escalation owner, and the mandate each role can exercise.
Authority and pause path
Clarify what the AI worker may do, what requires human review, who can pause or restrict the workflow, and what blocks expanded scope.
Evidence and exceptions
Keep review evidence, blocked conditions, exception paths, intervention notes, and lifecycle decisions tied back to the same operating record.
Cost and value review
Track cost and value as governance questions: cost per approved outcome, reviewed decision, exception, completed task, and avoided escalation where appropriate.
Public boundaryThis is planning and readiness language. It does not claim automated ROI measurement, live budget enforcement, regulatory approval, compliance conclusion, security authorization, audit opinion, or production authorization.
Govern AI agents as workers
Identity, scope, owners, lifecycle state, and evidence.
Control consequential action
Passport records, runtime checks, approval paths, and evidence trails before higher-risk movement.
Standardize AI operations
Intake, onboarding, lifecycle management, training, and reusable governance patterns.
Before Action
What happens before an AI worker acts?
This section is organized in two parts. First, it shows the decision checks that should happen before an AI worker acts. Second, it shows the governance records that provide the context and evidence for those checks.
1. Decision checks before action
Use these checks when an AI worker is about to touch tools, move data, send external communication, trigger cost, or support production-adjacent workflow.
Standing authority
Use the Passport to define purpose, owner, approved scope, prohibited actions, and review posture before relying on the worker.
Action-time check
Use runtime authorization to test the specific request against scope, approvals, evidence, data boundary, and target action.
Human decision path
When the request is sensitive, unclear, external, or higher risk, route it to accountable human review instead of letting it proceed automatically.
Replay-safe evidence
Preserve an execution receipt with non-secret decision facts so the path can be reconstructed without rerunning the action.
Fail-closed ruleIf owner, scope, approval, evidence, or current authority cannot be proven, the safer operating posture is to pause, block, require evidence, or escalate for human review.
2. Records that support the decision path
These records explain where the worker came from, what review has happened, what authority exists, and what evidence should be preserved.
Idea
Start with the business outcome, workflow, affected audience, and trust boundary.
Blueprint
Create a draft planning artifact with role, risk tier, governance controls, reviewers, blockers, and next steps.
Passport
Document owner, purpose, scope, permissions, prohibited actions, evidence, lifecycle state, and review posture.
Toll Gates
Identify where data, tool use, cost, autonomy, external communication, or consequential action needs review.
Human Review
Route unclear, sensitive, externally exposed, or higher-risk decisions to accountable human reviewers.
Runtime Permit
Represent short-lived scoped authority for one proposed action when the required context is present.
Action Broker
Evaluate whether the requested action should allow, deny, block, escalate, or require more evidence.
Evidence Trail
Use Stamps and evidence records to preserve review events, decisions, lifecycle changes, and outcomes.
Review boundaryBlueprints, Passports, Toll Gates, Stamps, Runtime Permits, Action Broker decisions, and evidence trails are governance records and review aids. They are not legal conclusions, compliance approvals, security authorizations, production approvals, audit opinions, formal attestations, or guaranteed outcomes.
Platform Operating Model
The governed AI worker operating layer.
Scaled Agents™ is not an AI security point tool or a model-provider wrapper. It is a provider-agnostic control plane for governing AI workers before they act across tools, data, systems, and workflows.
Security, identity, model providers, SaaS tools, cloud platforms, APIs, workflow systems, and MCP-compatible patterns are connected systems or execution environments around the control plane. They are not the control plane itself.
Passport
Governed identity, human owner, purpose, scope, permissions, lifecycle state, evidence, and review posture.
Toll Gates
Review and runtime gates for data, tools, cost, autonomy, external communication, and consequential action.
Stamps
Evidence markers for approvals, denials, actions, exceptions, lifecycle events, remediation, and outcomes.
Runtime Permit
Scoped, short-lived authority planning for a specific AI worker action when context and evidence support review.
Action Broker
Controlled action-path concept between AI workers and enterprise systems, using Passport, Toll, Policy, evidence, and review state.
Human Review
Accountable review workflow for approval, denial, escalation, exception, remediation, pause, or lifecycle decision.
Policy
Configurable decision logic and control rules for review, authorization, required evidence, and escalation paths.
Lifecycle Analytics
Visibility into status, risk, renewal, value, monitoring expectations, exceptions, incidents, and review-ready evidence context.
Connector Hub and MCP-compatible patterns belong around this operating layer as governed destinations or integration patterns. Scaled Agents should remain provider-agnostic and should not imply live connector governance or MCP server operation unless implementation evidence and approval exist.
Scaled Agents Passport
One governed operating record for AI workers.
The Passport connects identity, ownership, approved scope, review evidence, Toll Gates, lifecycle posture, and report views into one reviewable operating record for AI workers.
It gives teams a structured starting point for enterprise AI operations without treating a draft record, score, or readiness view as production authorization, legal approval, compliance conclusion, security approval, or an audit opinion.
Solutions Preview
Govern the path from idea to action.
Scaled Agents™ organizes the governance problems enterprise teams face as AI agents move closer to systems, tools, data, and business workflows: who owns the worker, what it may do, what requires review, what evidence exists, and what happens before consequential action.
Agent intake and assessment
Structured review paths for proposed AI workers, use cases, owners, risk tiers, prerequisites, and readiness evidence.
Toll Gates and Stamps
Governance checkpoints for recording reviewed transitions, access boundaries, evidence updates, exceptions, and activity records.
Portal preview
A preview customer experience for viewing agent status, ownership, risk posture, Passport state, support needs, and review records.
Governance Assurance
Readiness support for control domains, evidence boundaries, external-review preparation, and human-owned launch or pause decisions.
Governed by people. Powered by agents.
Every AI worker has a human partner. Every action has oversight.
Scaled Agents™ connects AI workers to named human owners for clarity, oversight, and accountability. The structure keeps decisions traceable, boundaries respected, and human judgment in control.
Named human partner
Every AI worker has a human owner responsible for purpose and performance.
Clear oversight
Humans set boundaries, review exceptions, and approve sensitive actions.
Structured hierarchy
AI workers operate inside defined scope, escalation paths, and review routes.
Action visibility
Key decisions and activity records are prepared for review and improvement.
Human oversight and approval includes an Executive Sponsor, AI Program Lead, Revenue and Product Lead, Technology and Operations Lead, Risk and Governance Lead, and Data and Customer Lead. Example AI workers include Intake Agent, Requirements Agent, Marketing Agent, Sales Enablement Agent, Documentation Agent, IT Service Desk Agent, Test Automation Agent, Release Readiness Agent, Security Operations Agent, Audit Support Agent, Assurance Agent, Legal Review Agent, Data Governance Agent, Business Intelligence Agent, Customer Success Agent, and Support Triage Agent. The legend distinguishes Human Role, AI Worker, Approval and Escalation, and Audit Trail responsibilities.
Featured Service
AI-Powered Company Blueprint
Draft planning support for business readiness, AI knowledge worker roles, human review, advisor preparation, and launch-readiness conversations before broader use.
DRAFT ONLY — CUSTOMER VERIFICATION AND PROFESSIONAL REVIEW REQUIRED BEFORE RELIANCE OR USE. AI-Powered Business Scaling Services are advisory, implementation-support, governance, and training services. AI-assisted outputs may be incomplete, inaccurate, outdated, or incorrect, and they do not promise revenue outcomes, compliance status, security credentials, audit signoff, legal conclusions, or operational success. Customers remain responsible for validation, approval, implementation, and qualified professional review where needed.
Training Paths
Training for leaders, builders, reviewers, and AI worker owners.
Most AI training teaches people how to use tools. Scaled Agents™ Training helps teams understand how to govern AI workers.
Scaled Agents™ Training provides role-based learning paths for teams preparing to govern AI workers across intake, design, review, oversight, evidence, escalation, and lifecycle management.
Training materials are being prepared for instructor-led delivery, self-paced online learning, customer enablement, and internal AI Worker QA review before publication. Each path helps teams understand AI worker governance and readiness boundaries without exposing proprietary Scaled Agents methods, scoring logic, internal workflows, or platform implementation details.
Course in construction
Self-paced training is being built.
The course structure, learning paths, and certificate boundaries are defined. Course lessons, quizzes, templates, and QA review materials are currently being prepared before enrollment opens.
Enrollment is not open yet. Certificates of Completion will be available only after the applicable training path is published.
Self-Paced Course Paths
Choose the role-based course path.
Each card below represents the public-facing course direction. The muted CTAs point visitors to the self-paced course preview while enrollment remains in preparation.
Foundation
Foundation for AI Worker Governance
For leaders, sponsors, business teams, and stakeholders who need a shared understanding of governed AI worker readiness.
- AI workers vs. AI tools
- Human accountability
- Ownership and role boundaries
- Readiness and evidence basics
Learner output: AI Worker Governance Role Map
Self-paced course previewBuild
Build Governed AI Workers
For builders, process owners, product teams, workflow designers, and implementation teams preparing AI worker use cases.
- Use case intake
- Workflow decomposition
- Access and authority boundaries
- Draft Agent Passport inputs
Learner output: AI Worker Blueprint
Self-paced course previewReview
Governance & Oversight
For reviewers, approvers, risk teams, compliance teams, security stakeholders, QA reviewers, and governance participants.
- Review responsibilities
- Risk and authority boundaries
- Evidence review
- Exception and escalation handling
Learner output: Governance Review Notes and Evidence Checklist
Self-paced course previewOperate
Specialize & Enable
For AI worker owners, operations teams, enablement leads, administrators, and trainers responsible for adoption and ongoing support.
- Agent owner responsibilities
- Monitoring expectations
- Change triggers
- Train-the-trainer basics
Learner output: Agent Owner Operating Plan
Self-paced course previewCertificate of Completion boundaries
Training completion records may be available for each published training path. They confirm course completion only. They do not grant AI worker approval, production deployment authority, legal assurance, compliance conclusion, security conclusion, audit approval, implementation approval, or permission to operate an AI worker.
Governance & Trust
Governance by default. Audit-aware by design.
Scaled Agents™ public materials use careful readiness language. They may describe framework-informed review, human oversight, Zero Trust principles, runtime governance concepts, nonhuman identity planning, cost and usage awareness, evidence mapping, data boundary metadata, pause and containment planning, and public planning templates, but they do not claim legal, regulatory, compliance, security, audit, government, live enforcement, or production use.
Governance Public Library
Public templates for AI worker readiness and review.
The Scaled Agents™ Public Library provides public preview templates, planning worksheets, and governance-readiness resources for teams preparing AI worker ownership, evidence, human review, security readiness, and controlled launch conversations.
Readiness templatesPlanning worksheets for AI worker ownership, intended use, review scope, and launch conversations.
Evidence supportCustomer-safe prompts for documenting assumptions, reviewed sources, evidence gaps, and decision context.
Human reviewMaterials that keep approvals, escalation, and accountability with the right human roles.
Who We Are
Human-led, governance-first AI operations.
Scaled Agents™ helps teams prepare AI-supported work for governed operation by clarifying ownership, boundaries, review paths, evidence needs, and human accountability before AI workers move closer to operational use.
AI workers may assist, draft, route, recommend, validate, and execute permitted work inside reviewed boundaries. Humans remain accountable for business, legal, security, compliance, customer, and operational decisions.
Connect with Scaled Agents on LinkedIn
Follow Scaled Agents for public updates, readiness guidance, AI worker governance ideas, and ways to interact with the Scaled Agents community.
Connect on LinkedIn
What this means in practice
Scaled Agents keeps the operating model anchored in human accountability, governed boundaries, and reviewable evidence before AI-supported work moves closer to execution.
- Human judgment stays accountable
- Governance boundaries come first
- Evidence supports operating trust
Forward-Deployed AI operating model
The deeper operating-model explanation lives on its own page, where the comparison, delivery flow, and governance boundaries have room to breathe.
Scaled Agents™ Advisor
Explore governed AI service readiness.
Use the Scaled Agents™ Advisor to explore AI worker governance, readiness questions, service planning, and practical next steps before a formal review.
Forward-Deployed AI
Explore human-led AI delivery patterns.
Use Scaled Agents™ Forward-Deployed AI to explore how business problems, workflow discovery, controls, reusable templates, and approval paths can move governed AI services from concept to controlled execution.
Engagement Path
What happens after a readiness conversation starts.
Scaled Agents™ begins with a controlled, high-level discussion before any customer data, portal access, implementation work, or reliance on draft outputs. The goal is to understand the AI worker opportunity, the human accountability model, and the review boundaries before recommending next steps.
Request
Share authorized, high-level planning context about the AI worker, business workflow, governance concern, or service-readiness need.
Scope
Clarify owners, intended use, data sensitivity, approval needs, lifecycle state, and whether the work belongs in platform readiness, service planning, or both.
Review
Identify the evidence, human review points, access boundaries, training needs, and professional-review dependencies before consequential use.
Plan
Prepare a practical next-step path for governance readiness, Passport planning, Toll Gate concepts, training, or AI-powered service support.
Form 1: Consultation Request
Start with a short consultation request.
Scaled Agents™ uses a two-step consultation process: submit a quick request first, then Scaled Agents reviews the request and follows up on the right consultation path.
Step 1: Share high-level, authorized contact details and a short description of what you want to discuss.
Step 2: After Form 1 is received, the page provides the next intake link so you can share additional planning context before the consultation.
Please share only authorized, high-level planning information. Do not include passwords, credentials, API keys, production secrets, regulated data, or confidential third-party material.