Public readiness - Governed AI worker planning materials are informational and support readiness conversations.

Customer-managed AI worker control plane

Govern AI Workers Before They Act. Start with before scale.

Govern the AI workers you build, buy, or deploy. Scaled Agents is a customer-managed governance Control Plane for AI Workers. It records ownership, approved scope, Toll Gates, Runtime Permits, Human Review, Stamps, and lifecycle evidence so higher-risk work can pause, proceed, or escalate through accountable review.

Request a Readiness Conversation Create a Scaled Agent
Scaled Agents Passport cover representing the governed operating record for AI workers.

Scope & Status

Scaled Agents provides the operating record. Customer owners make the formal decisions.

The public site supports planning, review preparation, and product evaluation. It explains how Passport records, review gates, evidence, and lifecycle state can help teams govern AI workers before scale without turning a preview artifact into production approval.

What Scaled Agents provides

A customer-managed control-plane pattern for AI worker ownership, Passport operating records, approved scope, review gates, evidence trails, and lifecycle visibility.

What customers decide

Legal, privacy, security, compliance, audit, production, and operational decisions remain with accountable customer owners and qualified reviewers.

What requires implementation

Live enforcement, connector execution, customer data storage, tenant identity, and regulated approvals require approved customer-managed implementation and release evidence.

Buyer Paths

Find the governance answer by role.

Scaled Agents should help each enterprise owner reach a clear answer quickly: how to scale AI adoption responsibly, control AI risk, operationalize AI Workers, and prove governance without treating readiness artifacts as formal approvals.

CIO

Scale AI adoption responsibly with an operating record for ownership, approved scope, lifecycle status, and review-ready evidence.

View Product Overview

CISO

Control AI risk by making authority, data boundaries, tool use, escalation paths, and pause conditions visible before higher-risk action.

View Security Architecture

CAIO

Operationalize AI Workers through consistent intake, Passport records, Toll Gates, Human Review, Stamps, and lifecycle visibility.

Create a Scaled Agent

CDO

Keep data boundaries and review expectations visible so AI-supported work can be assessed before sensitive or production-adjacent use.

Open Trust Center

GRC

Prove governance with source-linked records, evidence trails, owner decisions, framework-aware readiness views, and review boundaries.

View Framework Readiness

Audit

Inspect what was reviewed, what remains blocked, what evidence exists, and which accountable owner must decide before reliance.

View Oversight Controls

Go Deeper

Use the homepage for orientation. Use dedicated pages for depth.

The homepage now routes deeper Passport, oversight, shared responsibility, training, and resource detail to dedicated pages so buyers can move from orientation to the right review path without rereading the full operating model.

Operating Model

From AI worker idea to governed operating record.

Enterprise AI work breaks down when teams can build agents faster than they can answer basic operating questions: who owns the worker, what may it do, what requires review, what evidence exists, and what happens when risk, cost, or authority changes?

Scaled Agents is a licensed, customer-hosted or customer-managed platform for organizing that operating record. It connects intake, ownership, approved scope, review gates, evidence, and lifecycle visibility while keeping customer data, configurations, integrations, and production decisions under customer control.

The Passport is the signature operating record for each AI worker: owner, purpose, approved scope, permissions, review status, lifecycle state, and evidence. Deeper platform layers such as Toll Gates, Runtime Permits, Human Review, Stamps, and Action Broker planning build on that record when an AI worker moves closer to consequential action.

For the one-page product explainer, see what Scaled Agents does. For a step-by-step control path, see how a governed workflow moves from intake to reviewed next steps.

Scaled Agents operating model showing how Agent Passport, Agent Registry, Stamps, Tolls, services, review gates, and human-governed operations work together across the agent lifecycle.
Centralize the foundation. Scale the agents. Keep humans in control.
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 in a customer-hosted or customer-managed platform posture.

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.

The operating record is the foundation: named owner, approved scope, review path, evidence, and lifecycle state before higher-risk AI work moves toward production.

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.

Readiness layer

Public-safe planning content. Readiness templates, Advisor outputs, and Blueprint drafts help teams prepare review conversations. They do not approve production use.

Identity and authority layer

Shown in public preview records. Passport, Registry, Human Review, Toll Gate, Evidence Record, and Audit Export examples 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

Shown in public preview records. Passport, Registry, Toll Gate Decision, Evidence Record, Human Review Item, Audit Export Package, and default-deny review examples.

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 formal decisions.

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 Authority, runtime checks, approval validity, 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, trigger rights, approved scope, prohibited actions, validity window, 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 non-secret decision facts, Stamps, and evidence records so lineage can be reconstructed without rerunning the action.

Handoff continuity

Carry scope, state baseline, owner, approval status, and rollback context across agents, workflows, reviewers, and systems.

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.

Worked Example

One AI worker, one missing proof point, one visible stop.

A Procurement Intake AI Worker is asked to draft a supplier update. Its Passport shows the owner and approved purpose, but the requested destination is outside the approved tool boundary and the evidence record for external communication is missing. The Toll Gate blocks the action, creates an evidence item, and routes the request to Human Review instead of letting the worker send the update.

PassportOwner and purpose foundProcurement Operations owns the worker; draft supplier updates are in scope.
Toll GateEvidence gap detectedExternal-send evidence and destination approval are not present for this request.
DecisionBlocked, not sentThe action pauses because current authority cannot be proven.
Evidence TrailHuman review createdA review item and Stamp preserve why the request was blocked and what proof is needed next.

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, trigger rights, delegated authority, 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 before the decision window closes.

Human Review

Route unclear, sensitive, externally exposed, or higher-risk decisions to accountable reviewers who can change whether work proceeds.

Runtime Permit

Represent short-lived scoped authority for one proposed action when current purpose, evidence, scope, and approval context are 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, decision lineage, lifecycle changes, outcome validation, and rollback or correction context.

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 formal decisions.

Platform Operating Model

Govern, operate, and measure enterprise agent workforces.

Scaled Agents™ is not an AI security point tool, compliance-only product, agent directory, MCP tool, cloud platform, managed service provider, or model-provider wrapper. It is a licensed, customer-hosted or customer-managed, provider-agnostic governance and operations platform for governing AI agents before they act across tools, data, systems, and workflows.

Trust signalGovernance and evidence-readiness planning for regulated and high-trust AI environments, including CMMC and FedRAMP-aware conversations without certification, authorization, or compliance claims.

Security, identity, model providers, SaaS tools, GCP, AWS, Azure, Cloudflare, APIs, workflow systems, and MCP-compatible patterns are customer-selected connected systems or execution environments around the platform. Scaled Agents supports governed connection planning through approved connectors and helps customers govern agents around those connections through identity, ownership, policy, evidence, runtime review, and lifecycle controls.

Create a Scaled Agent Board & Executive Oversight Request a Readiness Conversation

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.

Board & Executive Oversight

Portfolio visibility into agent inventory, Passport coverage, risk exposure, value estimates, evidence completeness, incidents, exceptions, and scale or retire decisions.

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.

Board & Executive Oversight

AI governance should not only reduce risk. It should prove value.

Scaled Agents helps organizations connect every registered agent to ownership, oversight, evidence, risk exposure, policy exceptions, incidents, lifecycle state, and measurable business value.

Board Oversight Dashboard

Summarize registered agents, high-risk agents, pending reviews, expired evidence, human approval coverage, exceptions, material incidents, and regulatory readiness posture.

AI Risk Appetite

Define acceptable use cases, human-owned decisions, data classes, systems, escalation triggers, and pause, review, redesign, or retirement conditions.

Governance Economics

Use estimated, modeled, and risk-adjusted language to connect agent value, governance cost, cost avoidance, incident exposure, evidence confidence, and control coverage.

Open Board & Executive Oversight View Passport preview

Review boundaryBoard views, readiness mappings, scorecards, value estimates, Passports, Stamps, and evidence summaries support governance preparation and oversight. They are not legal advice, formal compliance findings, audit opinions, security authorizations, production approvals, or financial assurance results.

Governance Bridge

What Scaled Agents makes explicit: the Shared Responsibility Model for Recurring AI Agents.

Scaled Agents™ does not replace the governance model an organization already uses. It connects AI worker concepts back to ownership, risk appetite, control domains, review paths, documentation, auditability, and human accountability.

Known by design

  • AI workers need a defined purpose, human owner, lifecycle status, risk tier, and scoped authority.
  • Passports, registries, Toll Gates, execution receipts, evidence records, and review paths should make accountability visible.
  • Higher autonomy, sensitive data, external exposure, production systems, or business-critical workflows require stronger review.
  • AI workers may assist, draft, classify, summarize, recommend, route, and prepare evidence within approved boundaries.
  • Draft Blueprint and Advisor outputs are planning artifacts for review, not formal approval or production authorization.

Unknown until customer review

  • Which customer policies, committees, control frameworks, systems, data classes, and evidence standards apply.
  • Whether a specific AI worker should be approved, paused, redesigned, restricted, or moved toward implementation.
  • Which actions require management, risk, compliance, legal, security, privacy, model risk, operations, or audit review.
  • Whether the available evidence is sufficient for the organization's accountability, assurance, or oversight needs.
  • Whether any AI-supported action can proceed without human review in a specific business context.

The goal is to extend familiar enterprise governance into AI-enabled execution with clearer ownership, action-time review boundaries, control checkpoints, lifecycle visibility, and evidence trails.

Open Shared Responsibility Model
Scaled Agents

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 helps define the approved purpose and intended outcome early enough for Toll Gates, Runtime Permits, Human Review, and Monitoring to evaluate whether the worker remains purpose-aligned before higher-risk work moves forward.

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.

View sample Passport record View Passport Risk Map Request a Readiness Conversation
Scaled Agents Passport representing the governed operating record for AI workers
Passport, policy checks, approvals, monitoring, evidence, and readiness views should point back to the same governed record.

Identity and ownership

Shows who owns the AI worker, what purpose it serves, and where escalation should go.

Scope and boundaries

Documents permitted work, prohibited actions, data boundaries, tool access, and review limits.

Evidence and Toll Gates

Connects review evidence, approvals, lifecycle notes, and checkpoints before higher-risk movement.

Report views

Turns one Passport Scorecard into readiness, control, risk, and operating-decision views.

Generated from one Passport Scorecard.

Passport Report Views are launchers into the same underlying governance record. They help different reviewers inspect readiness, controls, risk, and decision posture without creating competing scores.

Scorecard outcome

Overall readiness status, blockers, evidence gaps, and approval posture.

Control mapping

How policy, review evidence, Toll Gates, and controls support the record.

Risk lens

Visible exposure across data, model behavior, agent action, security operations, and governance.

Operating decision

A reviewable posture for approved, conditional, blocked, paused, redesigned, or retired paths.

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.

Go Deeper View Public Resources

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.

Workforce governance roadmap

Coming-soon placeholders for runtime governance, Passport APIs, CFO and board reporting, multi-agent orchestration, and agent-to-agent control planning.

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 organization chart showing Executive Sponsor, AI Program Lead, human owner columns, AI Worker lists, approval and escalation, and audit trail responsibilities.
Structure adapts to the organization. Roles, AI workers, and approval paths should be reviewed before operational use.

Support Layer

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.

Go Deeper Start Intake

Support boundary. Advisory, blueprint, and training resources support evaluation and implementation of the licensed platform. They are not the core offer and do not replace customer validation, legal review, security review, compliance review, audit review, production approval, or accountable owner decisions.

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, lifecycle management, and timely, meaningful, purpose-aligned outcomes.

Self-paced training manuals are available for public review and readiness planning, with role-based paths for AI worker foundations, governance readiness, train-the-trainer enablement, Passport reviewer basics, and action-time governance. 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.

Go Deeper Download Training Manuals Request Training Preview
Manuals available

Self-paced manuals are ready for review.

Six self-paced manuals are available for public review and readiness planning. They include lessons, exercises, knowledge checks, answer keys, and artifact prompts while keeping training completion separate from production approval, credentialing, or permission to operate.

Download Training Manuals Request Training Preview
Scaled Agents training path overview for governed AI worker readiness and role-based enablement.
Visual overview only. The course cards below are the primary self-paced path surfaces.

Training manuals support education, readiness planning, and review preparation only. Completion or participation records, where used, confirm training activity only and do not grant approval, authorization, credential status, or permission to operate an AI worker.

Self-Paced Course Paths

Choose the role-based course path.

Each card below represents a public-facing course direction. Use the catalog for course detail and the Public Resources library for available manual downloads.

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 preview

Build

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 preview

Review

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 preview

Operate

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 preview

Training completion record boundaries

Scaled Agents is not issuing certificates. Training completion or participation records may be used for internal tracking only. They do not grant AI worker approval, production deployment authority, legal assurance, compliance conclusion, security conclusion, audit approval, implementation approval, credential status, 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.

Go Deeper View Public Resources
Scaled Agents readiness assessment visual preview
Readiness assessment preview for evidence-backed governance conversations and customer review planning.

Governance Public Resources

Public templates for AI worker readiness and review.

Scaled Agents™ Public Resources provide public 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.
Business Case AI Governance Policy Template ISO 42001 Readiness Go Deeper Request Guidance

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
Scaled Agents Principles graphic showing six principles for responsible AI adoption, including human judgment, accountability, explainability, and responsible automation.
Human-centered operating doctrineThe Scaled Agents Principles™ define how AI-enabled work should create time, expand options, and increase capacity while humans retain purpose, context, accountability, and judgment.

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.

Go Deeper Request a Readiness Conversation

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.

Go Deeper Partners Journey Planner Open AI Governance Advisor GPT Request a Readiness Conversation

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.

Go Deeper Open Forward-Deployed AI GPT Request a Readiness Conversation

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.

01

Request

Share authorized, high-level planning context about the AI worker, business workflow, governance concern, or service-readiness need.

02

Scope

Clarify owners, intended use, data sensitivity, approval needs, lifecycle state, and whether the work belongs in platform readiness, service planning, or both.

03

Review

Identify the evidence, human review points, access boundaries, training needs, and professional-review dependencies before consequential use.

04

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.

Your submission was received. A representative at Scaled Agents may reach out to you to schedule a consultation.

Next intake step: Form 2: Detailed Consultation Intake.

Public readiness - Materials support readiness planning and do not authorize production use.