Designing Systems

Infrastructure for Intelligent Management

Engineering the agentic enterprise.

Create an executable digital twin covering strategy, policy, authority, and work.

Learn moreSee how Atlas works
Atlas — one interconnected enterprise digital twinOne enterprise digital twin, shown as six interconnected layers. Together they span Strategy & Outcomes; Governance, Risk & Policy; Organization, Roles & Decision Rights; Capabilities & Services; Data, Systems & Knowledge; People & AI Agents. Each box is a part of the organization with its own rules and responsibilities. A change in one part can ripple through direct and indirect relationships within and across layers. Connections are illustrative. Hover or focus to open the layers. Use arrow keys to explore blocks. Click, Enter or Space to keep a selection, and Escape to reset it. Layer names remain visible on the right-hand edges. During exploration, the selected part is identified beside its block.People & AI AgentsLeadership — People & AI AgentsDomain experts — People & AI AgentsService teams — People & AI AgentsAI agents — People & AI AgentsData, Systems & KnowledgeOperational data — Data, Systems & KnowledgeBusiness systems — Data, Systems & KnowledgeShared knowledge — Data, Systems & KnowledgeDecision records — Data, Systems & KnowledgeCapabilities & ServicesCore capabilities — Capabilities & ServicesBusiness processes — Capabilities & ServicesCustomer services — Capabilities & ServicesOrganization, Roles & Decision RightsBusiness units — Organization, Roles & Decision RightsTeams & roles — Organization, Roles & Decision RightsDecision rights — Organization, Roles & Decision RightsAccountability — Organization, Roles & Decision RightsReporting lines — Organization, Roles & Decision RightsGovernance, Risk & PolicyRisk appetite — Governance, Risk & PolicyService policies — Governance, Risk & PolicyRegulatory obligations — Governance, Risk & PolicyAI governance — Governance, Risk & PolicyStrategy & OutcomesStrategic priorities — Strategy & OutcomesCustomer outcomes — Strategy & OutcomesOperating objectives — Strategy & OutcomesPerformance measures — Strategy & OutcomesStrategy & Outcomes. Strategic priorities: 3 direct connections and 2 indirect connections. Illustrative organizational relationships.

Engineering reliable systems for responsible AI at scale

Three principles. One living model. Governance built into the way work happens.

Three foundational principles

1Zero TrustEvery actor must earn authorization for every action, with no implicit trust.

A role is not a blanket permission. Identity, authority, and the action being requested must agree before work proceeds.

2STP Techniques & PrinciplesEnforce safety constraints through an accurate sociotechnical model.

Safety depends on the relationships between people, technology, and work. Model their interactions so constraints belong to the system itself.

3Digital TwinGovern AI through explicitly identified roles, authority boundaries, and rules.

The twin connects institutional intent to the people, agents, and processes carrying it out. The model remains part of execution.

Executable Digital Twin

Strategy & GovernancePolicy intent and board-level decisions propagate down.

Objectives, policies, and obligations establish the intent that every other layer must carry into practice.

Roles & IdentityRole definitions, authority boundaries, and institutional identity.

Define who may decide, who may act, and where responsibility or human review is required.

ProcessesWorkflows, data categories, and care delivery.

Connect each workflow and handoff to its data, rules, and safety constraints, including care delivery where applicable.

OperationsHuman and agent actors, record access, and execution.

Apply the model where work happens: to each actor, each access request, and each action.

Policy as Execution

Policy intent is translated into executable rules (EBL) via LLMs, constructed and maintained by AI traversal.

The Inversion

Build the governed organizational space first, then design and deploy AI systems within it.

Specific institutional identity delivers safety and security from the same formal object.

Key outcomes

Policy as ExecutionPolicy intent becomes executable rules via LLMs.

Rules are connected to the actions they govern.

Governed AI OperationAI operates within defined authority, roles, and controls.

Delegation and limits are part of the operating model.

TraceabilityDecisions, actions, and data are recorded and auditable.

Execution can be traced back to its context and authority.

Safety & Security AlignmentSafety and security delivered from the same formal model.

The same institutional model informs both kinds of control.

RetrofittedRetrofit governance afterwardsHigher risk, harder, less effective

Existing behavior must be reconciled with authority and safety constraints after deployment.

vs.
Responsible EngineeringBuild governed organizational space firstDesign AI systems inside it

Authority, rules, and controls inform the design from the start.

Atlas: The AI-native enterprise digital twin

Enterprise evidence becomes an executable model of the organization that continually learns from execution

Enterprise Evidence. Start with what the enterprise knows: documents, connected systems, process logs, databases, and the knowledge held by its people.

Documents

Policies, procedures, and business documents provide the enterprise’s recorded intent.

Systems & APIs

Connected systems describe the capabilities and interfaces through which work happens.

Event / Process Logs

Events and process histories show the paths work actually takes.

Databases

Structured records provide the entities, relationships, and operational facts behind the work.

Human Knowledge

People add context, experience, and exceptions that are not fully captured in systems.

Extract &
connect

General +
Domain Models

Generative
Agents

Expert
Design

Human
Validation

Risk, Controls & GovernanceAssess · Mitigate · Monitor

AI extraction and expert refinement work together. Human validation and risk controls remain part of the modeling process.

Organization

People, roles, responsibilities, and the relationships between them.

Processes

Workflows, decisions, handoffs, and the dependencies that connect work.

Information

The information and evidence used to understand work and support decisions.

Applications

The business applications through which people and agents perform work.

Technology

The technical capabilities and dependencies supporting the operating model.

Infrastructure

The underlying services and environments on which execution depends.

Authority / Policy

Permissions, obligations, limits, and review points that govern activity across the model.

Continually updated model of the enterprise

Plan

Form a course of action using enterprise objectives, context, and constraints.

Decide

Evaluate choices within the authority and policy defined by the enterprise.

Coordinate

Connect work across people, agents, and systems with explicit responsibilities.

Act

Carry out permitted actions and retain the evidence of what happened.

Within enterprise context, policy, and authority

Measure

Compare execution and outcomes with the objectives and constraints in the model.

Learn

Use evidence from execution to understand what worked and where the model needs attention.

Refine

Bring new understanding back into the model through expert refinement and validation.

Evolve

Carry the refined model into the next cycle of governed execution.

Atlas continuously understands, models, governs, executes, and evolves the enterprise through a closed-loop digital twin lifecycle.