Enterprise AI Architecture Reference

The persistent representation of who or what is participating in the system. Enterprise AI requires separate treatment of the identity of the person using the system and the identity of the agent acting within it.

Foundational Architecture

Establish the distinct identities upon which authority, responsibility, relationships, state, and execution decisions depend.

Identity ambiguity, identity conflation, and loss of separation between the human principal and the agent acting on that principal's behalf.

• • Establish authority
• • Grant permission
• • Guarantee that an agent remains aligned with its assigned identity
• • Determine admissibility

The operating reality, assumptions, objectives, constraints, and reference conditions within which a system interprets information and makes decisions.

Foundational Architecture

Provide the contextual reality that gives meaning to system behavior and execution decisions.

Systems reasoning from the wrong operational reality.

• • Authorize execution
• • Replace governance
• • Guarantee truth
• • Prevent drift

The architectural limits that define where authority, responsibility, execution, and acceptable behavior begin and end.

Foundational Architecture

Constrain execution to defined operating conditions and responsibilities.

Unbounded execution beyond intended operating limits.

• • Guarantee compliance
• • Correct drift
• • Replace governance
• • Determine outcomes

The progressive divergence between a system's current operating condition and the authoritative condition required for coherent execution.

Foundational Architecture

Describe the loss of coherence across one or more architectural layers.

Progressive loss of coherence leading to unreliable or unsafe operation.

• • Explain root cause
• • Restore alignment
• • Determine admissibility
• • Authorize execution

The architectural process of restoring alignment between the current operating condition and the authoritative condition required for coherent execution.

Foundational Architecture

Restore coherence before execution continues.

Persistent divergence after drift has occurred.

• • Guarantee correctness
• • Replace governance
• • Replace validation
• • Prevent future drift

The architectural layer responsible for data acquisition, storage, quality, lineage, transformation, classification, access, and availability.

Data Architecture

Ensure that data used by models, agents, workflows, and business systems is available, traceable, appropriately protected, and fit for its intended use.

AI systems operating on missing, corrupted, stale, inaccessible, untraceable, or improperly governed data.

• • Determine whether an AI action is admissible
• • Establish execution authority
• • Govern agent behavior
• • Guarantee that valid data produces a valid decision

The architectural layer responsible for inference, prediction, classification, generation, model behavior, and model-level evaluation.

Model & Inference Architecture

Transform inputs into probabilistic outputs that support language generation, analysis, prediction, reasoning, or classification.

The need to produce useful probabilistic outputs from complex or unstructured information.

• • Represent the complete AI system
• • Possess organizational authority
• • Determine whether an action may execute
• • Guarantee factual or operational correctness

The architectural layer responsible for planning, tool use, delegation, memory, workflow participation, task execution, and coordination across one or more steps.

Agent & Orchestration Architecture

Coordinate model reasoning, tools, state, workflows, and external systems to pursue an objective.

The inability of a single model inference to coordinate multi-step work across tools, systems, and changing conditions.

• • Possess authority merely because it can act
• • Guarantee that the objective is appropriate
• • Determine admissibility by itself
• • Replace runtime execution control

The architectural and organizational layer that defines policy, authority, accountability, decision rights, evidence requirements, risk boundaries, and acceptable operating conditions.

Governance & Authority Architecture

Define who may decide, what may be delegated, which constraints apply, what evidence is required, and what conditions must govern AI use.

AI systems operating without clear accountability, policy, decision rights, authority boundaries, or enforceable governance conditions.

• • Enforce itself automatically at runtime
• • Determine current system state
• • Prevent execution unless connected to technical controls
• • Replace the execution boundary

The architectural layer that evaluates current state, authority, policy, evidence, constraints, and consequence immediately before an action crosses into execution.

Runtime State & Execution Control

Determine whether a specific proposed action may execute under the conditions that exist now.

Actions executing after state, authority, evidence, policy, or operating conditions have become invalid or inadmissible.

• • Reconstruct the complete history of the system
• • Replace organizational strategy
• • Guarantee model quality
• • Assume that prior approval remains valid

The architectural layer responsible for detecting divergence, validating the system against authoritative external state, restoring alignment, and preventing continued execution under drifted conditions.

Correction & Coherence Control

Restore coherence when identity, frame, boundary, authority, state, data, model behavior, or execution has diverged from the authoritative condition.

Systems continuing to operate from internally reinforced, stale, invalid, or drifted state without an independent mechanism for restoring alignment.

• • Define governance policy
• • Guarantee that every correction is valid
• • Eliminate future drift
• • Replace the need for current-state validation

A computational model that produces predictions, classifications, recommendations, generated content, or other outputs from learned patterns.

Model & Inference

Transform inputs into probabilistic outputs that may support reasoning, analysis, prediction, or generation.

The need to interpret complex inputs and produce useful probabilistic outputs.

• • Establish organizational authority
• • Determine whether execution is permitted
• • Guarantee factual correctness
• • Provide governance by itself

A probabilistic model trained to process and generate language by estimating likely token sequences from supplied context.

Model & Inference

Generate, transform, summarize, classify, and reason over language-based information.

Flexible language interpretation and generation across broad domains.

• • Know whether its output is true
• • Possess execution authority
• • Maintain organizational accountability
• • Determine whether an action is admissible

The process through which a trained model evaluates supplied inputs and produces an output.

Model & Inference

Apply learned statistical relationships to a current request or operating context.

The need to generate an output from information not explicitly programmed as fixed rules.

• • Validate organizational state
• • Establish decision rights
• • Guarantee repeatable output
• • Authorize downstream action

The instruction, question, context, or structured input supplied to a model for inference.

Model Interface

Shape the model task and provide the information required to produce an output.

The need to direct model behavior for a specific inference request.

• • Create durable authority
• • Guarantee compliance
• • Prevent state drift
• • Replace system architecture

An output presented by an AI system that appears plausible but is unsupported, incorrect, fabricated, or inconsistent with authoritative evidence.

Inference & Grounding Failure

Describes a class of output failure that may originate in model inference, context construction, retrieval, memory, orchestration, or the system’s failure to verify the result.

Identification of unsupported, fabricated, or insufficiently verified output produced or admitted by the system.

• • Prove that the model alone caused the failure
• • Identify the precise architectural source of the failure
• • Describe authority failure
• • Describe state drift
• • Describe unsafe execution by itself

Information made available to a model or system for the current inference, workflow, or decision.

Context Construction

Provide the immediate information required to interpret a request or operating situation.

Model operation without sufficient situational information.

• • Persist automatically
• • Establish that the information is true
• • Guarantee completeness
• • Create authority

The bounded amount of information a model can consider during a single inference operation.

Model Interface

Provide the working information available to the model during generation.

The need to bound and manage the information supplied during inference.

• • Create permanent memory
• • Preserve complete history
• • Guarantee the relevance of included information
• • Validate supplied information

Persisted information retained across interactions, sessions, workflows, or system operations.

State & Persistence

Allow a system to retain facts, preferences, relationships, prior decisions, or workflow state.

Loss of information across interactions or workflow boundaries.

• • Guarantee truth
• • Resolve conflicting memories automatically
• • Determine relevance by itself
• • Establish current authority

A pattern in which information is retrieved from external sources and supplied to a model before generation.

Retrieval & Context Construction

Ground model output in selected external information.

Model generation without access to relevant external information.

• • Guarantee that retrieved information is correct
• • Resolve authority
• • Prevent execution against invalid state
• • Create governance

A numerical representation of content used to compare semantic similarity.

Retrieval

Support semantic search, clustering, matching, and retrieval.

The inability of strict keyword matching to recover conceptually related information.

• • Prove semantic equivalence
• • Validate truth
• • Establish authority
• • Determine execution admissibility

A software component that uses models, tools, state, and workflow logic to pursue an objective across one or more steps.

Application & Orchestration

Coordinate reasoning and action across tasks that require more than a single inference.

The need to perform multi-step work across tools and changing conditions.

• • Possess authority merely because it can act
• • Guarantee correct planning
• • Guarantee safe execution
• • Replace governance

A multi-step workflow in which one or more AI agents reason, use tools, maintain state, make intermediate decisions, and handle exceptions.

Workflow Orchestration

Coordinate complex work that cannot be completed through a single fixed request.

Rigid automation that cannot adapt to changing workflow conditions.

• • Guarantee authority
• • Guarantee admissibility
• • Guarantee that the workflow objective is appropriate
• • Eliminate the need for human decision rights

The coordination of agents, tools, dependencies, execution paths, handoffs, state, and approval points.

Workflow Orchestration

Manage how multiple components collaborate to complete a larger objective.

Uncoordinated multi-agent behavior and broken workflow dependencies.

• • Determine whether the objective is permissible
• • Create execution authority
• • Prevent collusion automatically
• • Guarantee coherent state across agents

A mechanism through which a model or agent invokes an external function, service, database, application, or device.

Application Integration

Allow an AI system to retrieve information or perform actions outside the model.

Model isolation from external systems and operational capabilities.

• • Establish permission to use the tool
• • Guarantee valid parameters
• • Guarantee that the resulting action is admissible
• • Provide accountability

A protocol for exposing tools, resources, and contextual capabilities to AI applications through a standardized interface.

Integration

Standardize how AI applications discover and use external capabilities.

Fragmented, one-off integration between AI applications and external capabilities.

• • Determine whether a tool should be called
• • Authorize execution
• • Validate current state
• • Replace runtime governance

The measurement of model or system behavior against defined criteria.

Quality Assurance

Assess quality, accuracy, safety, consistency, or task performance.

Unknown or unmeasured model and system performance.

• • Authorize execution
• • Establish authority
• • Prevent an action from occurring
• • Guarantee valid current state

The ability to understand system behavior through logs, traces, metrics, events, and internal state signals.

Telemetry & Operations

Expose what the system did, how it behaved, and where failures occurred.

Invisible system behavior and failures that cannot be diagnosed.

• • Prevent unsafe execution
• • Create policy
• • Authorize action
• • Correct drift by itself

Continuous or periodic observation of system conditions, outputs, performance, events, or risk indicators.

Operations

Detect changes, anomalies, threshold violations, and emerging failures.

Undetected changes and operating conditions.

• • Stop execution unless connected to a control
• • Establish authority
• • Repair drift automatically
• • Determine admissibility by itself

Operational data emitted by a model, agent, workflow, or system during execution.

Operations & Evidence

Provide measurable signals about behavior and system condition.

Lack of operational evidence.

• • Interpret itself
• • Authorize execution
• • Prove that a state is valid
• • Replace an execution gate

The policies, responsibilities, decision structures, oversight mechanisms, and controls used to direct and constrain AI adoption and operation.

Organizational Governance

Define how AI should be selected, approved, operated, reviewed, and held accountable.

Unclear accountability, unmanaged risk, and inconsistent organizational control.

• • Automatically enforce itself at runtime
• • Determine current system state
• • Prevent execution unless connected to controls
• • Replace technical architecture

The legitimate right to make a decision, approve an action, delegate responsibility, or permit execution.

Governance & Execution

Define who or what may decide and act under specified conditions.

Actions performed without legitimate decision rights.

• • Guarantee that an action is correct
• • Guarantee that conditions remain unchanged
• • Persist automatically after delegation
• • Replace identity

The explicit assignment of who may make which decisions, under what conditions, with what accountability.

Organizational Governance

Prevent ambiguity about human and machine responsibility.

Unclear ownership and accountability for consequential decisions.

• • Guarantee that the decision is executed correctly
• • Replace authorization controls
• • Validate current state
• • Prevent authority drift by itself

Evidence describing the origin, transformation, lineage, handling, and history of data, models, state, decisions, or outputs.

Evidence & Audit

Support traceability, reconstruction, audit, explanation, and accountability.

Unknown origin, lineage, and causal history.

• • Authorize the next action
• • Determine current admissibility
• • Prevent unsafe execution
• • Replace current-state validation

Information accepted by a system or organization as support for a claim, state determination, approval, decision, or execution condition.

Governance & Validation

Provide a defensible basis for decisions and controls.

Decisions and actions unsupported by defensible information.

• • Validate itself
• • Remain current automatically
• • Guarantee correct interpretation
• • Authorize every action derived from it

The externally validated representation of the system and its relevant operating conditions immediately before an execution decision.

Runtime State Control

Provide the authoritative operating state used to evaluate whether a proposed action may execute.

Execution based on stale, incomplete, invalid, or internally assumed state.

• • Explain the entire history of the system
• • Guarantee future state
• • Replace provenance
• • Define organizational strategy

Independent comparison of system state, evidence, authority, identity, frame, or operating conditions against an authoritative source outside the executing system.

Validation & Runtime State Control

Establish whether the state used for an execution decision remains aligned with authoritative external reality.

Systems validating their own assumptions and continuing to execute from stale, fabricated, incomplete, or internally reinforced state.

• • Correct drift by itself
• • Define governance policy
• • Guarantee that the external source is current or authoritative
• • Determine admissibility by itself

The architectural checkpoint at which a proposed action is evaluated against current state, authority, policy, evidence, constraints, and consequence before execution.

Runtime Execution Control

Prevent an action from crossing into execution unless required conditions are satisfied.

Actions executing without a final runtime control decision.

• • Reconstruct system history
• • Optimize the business objective
• • Replace organizational governance
• • Guarantee the quality of model reasoning

The boundary-resident persistence and control layer that records validated state, authority, evidence, admissibility decisions, and resulting state transitions across each system turn or execution event.

Coherence Boundary & Runtime State Control

Maintain authoritative continuity across turns by recording the state used for execution, the decision made at the execution boundary, and the resulting state before the next turn begins.

Stateless execution, stale state, untraceable transitions, and divergence between the state a system believes it holds and the state actually governing execution.

• • Determine admissibility by itself
• • Replace current-state validation
• • Guarantee that recorded evidence is authoritative
• • Reconstruct events that were never recorded

The determination that a proposed action satisfies every required execution condition within the current validated state.

Runtime Execution Control

Determine whether a specific action may execute now.

Unsafe, unauthorized, unsupported, or out-of-bound execution.

• • Measure model performance
• • Explain how the system reached its current state
• • Predict business success
• • Replace provenance or audit

Governance mechanisms that operate while the system is active and influence or control execution under current conditions.

Runtime Control

Translate governance requirements into operational enforcement.

Governance that exists in policy but disappears during execution.

• • Replace policy development
• • Eliminate the need for evidence
• • Guarantee model correctness
• • Explain historical causation by itself

An architectural control that prevents, interrupts, or redirects execution when system state crosses a defined safety or admissibility boundary.

Safety & Runtime Control

Prevent unsafe execution when required conditions are no longer satisfied.

Continued execution after a critical operating boundary has been crossed.

• • Explain why the condition occurred
• • Perform root-cause analysis
• • Replace monitoring
• • Guarantee service continuity

A rule, filter, constraint, prompt, policy, or control intended to influence or restrict AI behavior.

Behavioral Constraint

Reduce the likelihood of undesired model or system behavior.

Unconstrained or undesirable model and application behavior.

• • Guarantee enforcement
• • Determine current validated state
• • Create execution authority
• • Replace a fail-safe

Degradation or change in model performance as operating data or conditions move away from those represented during development or validation.

Model Operations

Describe changing model performance under evolving conditions.

Model performance degradation over time.

• • Describe every form of system drift
• • Describe authority loss
• • Describe invalid workflow state
• • Describe execution admissibility

A change in the statistical characteristics, distribution, quality, or meaning of operating data.

Data Operations

Identify divergence between expected and observed data conditions.

Models and systems operating on data conditions they were not designed to handle.

• • Determine whether execution should stop
• • Explain authority failure
• • Correct the data automatically
• • Describe every change in system state

Divergence between the system state being used for decisions and the authoritative state that should govern operation.

Runtime State

Identify when execution is being based on stale, incomplete, corrupted, or internally assumed state.

Execution against a state that no longer reflects reality.

• • Describe model performance alone
• • Explain all historical causes
• • Correct itself
• • Establish authority

Divergence between the identity an actor or agent is required to maintain and the identity, role, purpose, authority scope, or accountable representation under which it is currently operating.

Identity & Runtime Control

Identify when a user, agent, service, or delegated actor no longer matches the identity conditions required for coherent execution.

Agents or actors continuing to operate under substituted, stale, expanded, ambiguous, or misaligned identity.

• • Prove malicious intent
• • Describe authentication failure alone
• • Describe authority drift by itself
• • Correct identity automatically

Divergence between the operating frame used by the system and the authoritative objectives, assumptions, constraints, references, or conditions that should govern interpretation and action.

Context & Runtime State

Identify when a system is reasoning or acting from an obsolete, incomplete, or incorrect operating reality.

Systems continuing to interpret evidence and pursue actions within a frame that no longer reflects the governing reality.

• • Describe model drift alone
• • Establish authority
• • Correct the operating frame automatically
• • Determine admissibility by itself

Loss of alignment between the authority originally granted and the authority currently exercised by a model, agent, workflow, user, or system.

Authority & Runtime Control

Detect when permissions, delegation, scope, role, or decision rights no longer match the current operating situation.

Systems continuing to act under authority that is stale, expanded, inherited, or no longer valid.

• • Describe identity alone
• • Describe model accuracy
• • Prove malicious intent
• • Correct authority automatically

Independent validation against an authoritative external reference used to restore alignment before execution continues.

Correction & Runtime Control

Restore coherence by comparing the current operating condition against authoritative external state rather than relying solely on the system's internal interpretation.

Self-reinforcing divergence in which a system validates its own incorrect state.

• • Define governance policy
• • Repair historical provenance
• • Replace admissibility
• • Guarantee the external reference is authoritative

The represented person, agent, service, organization, role, or system associated with an interaction or action. In agentic systems, human identity and agent identity must remain distinct even when the agent acts on behalf of the human.

Security, Identity & Access

Establish who initiated the interaction, which agent performed the work, and which identity is accountable for each action.

Actions that cannot be attributed separately to the human principal and the agent that executed them.

• • Establish permission by itself
• • Establish decision authority
• • Prove that an agent remained within its assigned role
• • Determine whether a specific action is admissible

The identity of the person, organization, or human principal initiating, directing, approving, or receiving the result of an AI interaction.

Security, Identity & Access

Establish the human or organizational principal associated with the request, decision, delegation, and resulting accountability.

Loss of accountability for who initiated, approved, or benefited from an AI-driven action.

• • Define the identity of the agent performing the work
• • Automatically transfer all user authority to the agent
• • Guarantee that the user is authorized for every requested action
• • Determine execution admissibility

The persistent identity, assigned role, operating purpose, authority scope, and accountable representation of the AI agent performing work within the system.

Security, Identity & Access

Establish which agent acted, what identity it was required to maintain, whose authority it was operating under, and what boundaries applied to its behavior.

Agents acting under ambiguous, substituted, stale, expanded, or drifted identity.

• • Inherit all authority of the user automatically
• • Guarantee that the agent remains aligned with its assigned role
• • Replace authentication or authorization
• • Determine admissibility by itself

The process of verifying a claimed identity.

Security & Identity

Determine whether an actor is who or what it claims to be.

Unknown or falsely represented identity.

• • Determine what the actor may do
• • Establish business authority
• • Guarantee current role alignment
• • Authorize a specific execution

The determination that an authenticated identity has permission to access a resource or perform an operation.

Security & Access Control

Restrict access and actions according to assigned permissions.

Access or operations performed without assigned permission.

• • Guarantee that the action is appropriate now
• • Establish organizational decision rights
• • Validate current operating state
• • Replace admissibility

The principle that identities and systems should receive only the permissions required for their current responsibilities.

Security & Access Control

Limit the impact of compromise, error, misuse, and unintended behavior.

Excessive access and unnecessary operational capability.

• • Determine whether a permitted action is admissible
• • Validate system state
• • Prevent all privilege escalation
• • Define business accountability

A security model that avoids assuming trust based solely on network location, prior access, or organizational position.

Security Architecture

Require explicit verification for access under changing conditions.

Implicit trust granted from location, history, or inherited access.

• • Determine business suitability
• • Establish execution admissibility automatically
• • Replace governance
• • Guarantee coherent system state

A layered architecture describing how identity, frame, boundary, drift, and correction determine whether an AI system remains coherent.

System Architecture

Provide a structural model for understanding AI system coherence and failure.

AI failure analyzed as isolated model behavior rather than architectural loss of coherence.

• • Describe only model drift
• • Replace implementation-specific controls
• • Assume governance documents enforce themselves
• • Treat the model as the complete system

The SAMIRAC architecture for evaluating and enforcing whether proposed AI execution remains admissible under current validated conditions.

Execution Governance

Translate authority, policy, evidence, state, risk, and constraints into an execution decision.

AI actions executing without proof that required conditions remain satisfied.

• • Depend on model confidence alone
• • Treat initial approval as permanent
• • Replace business governance
• • Assume observability prevents execution

A shared execution-control boundary governing the combined action of multiple agents, systems, tools, or delegated actors.

Distributed Execution Governance

Evaluate the admissibility of combined behavior that no single component controls independently.

Individually permissible actions combining into an impermissible collective outcome.

• • Assume each agent is safe because it is individually authorized
• • Treat orchestration as governance
• • Ignore emergent collective behavior
• • Replace agent-specific controls

A durable record of the state, evidence, authority, policy, constraints, decision, and outcome associated with an execution event.

Evidence & Execution Governance

Make execution decisions reconstructable and defensible.

Execution decisions that cannot later be explained, audited, or defended.

• • Authorize action by itself
• • Replace current-state validation
• • Prevent unsafe execution
• • Guarantee the integrity of upstream evidence

Demonstrable alignment between an implemented system and the required architectural, governance, authority, state, evidence, and execution-control specification.

Architecture Assurance

Establish whether implementation matches the control model it claims to implement.

Systems claiming governance without implementing the required controls.

• • Mean that documentation exists
• • Mean that policy was approved
• • Guarantee permanent compliance
• • Replace runtime validation