The Alignment Architecture

Alignment as the Foundation of Coherent Action in AI-Mediated Systems

Arqua Architecture Paper

June 2026

Mark Tovey, Arqua Pty Ltd

Execution alone does not guarantee alignment.

AI-mediated systems can generate plans, coordinate workflows, recommend interventions and automate action. But a system can become highly efficient while drifting away from the intent, authority, values and constraints that originally justified its existence.

The Alignment Architecture defines a three-layer model for preserving coherent action in AI-mediated systems:

Meaning → Execution → Admissibility

Meaning preserves intent.

Execution creates action.

Admissibility governs consequence.

When these remain connected over time, systems preserve coherence.

Abstract

As artificial intelligence becomes increasingly embedded within personal, organisational and institutional systems, a new architectural challenge is emerging.

Modern systems are increasingly effective at execution.

They convert objectives into plans, plans into workflows, workflows into actions, and actions into measurable outcomes. AI systems now accelerate this pattern by generating recommendations, coordinating tasks, summarising context, prioritising decisions and influencing operational behaviour.

However, execution alone does not guarantee alignment.

A system can become highly efficient while drifting away from the intent, authority, values and constraints that originally justified its existence. This creates a growing architectural problem: the separation of meaning from execution, and execution from consequence.

This paper introduces the Alignment Architecture: a three-layer architectural model connecting Meaning, Execution and Admissibility into a coherent operational framework.

In this paper, alignment does not refer primarily to model behaviour, preference tuning or AI safety techniques. It refers to the architectural continuity between originating intent and consequential action.

The Alignment Architecture proposes that trustworthy AI-mediated systems require:

1. a Meaning Layer that preserves intent, mandate, purpose and constraint;
2. an Execution Layer that converts intent into operational action;
3. an Admissibility Layer that governs whether consequential action is permitted to occur at the moment it becomes real.

When sustained over time, this alignment produces coherence: a condition in which meaning, action, evidence and consequence remain mutually consistent across changing contexts.

The paper positions Alignment Architecture as a bridge between Codex Resonance’s research into coherence, meaning, lineage, trust and constraint, and Arqua’s enterprise architecture for execution admissibility.

In practical terms:

Alignment is the architecture.

Admissibility is the control boundary.

Coherence is the condition preserved.

Introduction

Most contemporary systems are designed around two concerns.

The first is understanding what should happen.

The second is executing what should happen.

A growing number of systems now perform both functions at increasing speed and sophistication.

Machine learning predicts.

Large language models reason.

Workflow engines orchestrate.

Automation platforms execute.

Digital platforms measure, optimise and adapt.

Yet a fundamental architectural question remains unresolved:

Who, or what, determines whether a proposed action remains appropriate at the moment it becomes real?

This question becomes increasingly important as AI systems move from advisory roles into operational environments where recommendations, prompts, workflows, approvals, escalations and automated actions create real-world consequences.

The challenge is no longer simply intelligence.

The challenge is not merely automation.

The challenge is alignment.

More specifically, the challenge is preserving the relationship between meaning and consequence across distributed, AI-mediated execution environments.

Where that relationship is preserved over time, systems become coherent.

Where that relationship fragments, systems may continue to execute, but they no longer reliably act in accordance with their originating purpose.

This is why alignment must be treated as an architectural concern rather than a statement of intent.

What Alignment Means in This Paper

The term alignment is widely used in artificial intelligence.

In many AI contexts, alignment refers to whether a model behaves consistently with human preferences, values, safety constraints or intended outputs.

That is not the primary focus of this paper.

This paper uses alignment in an architectural sense.

Alignment is the maintained relationship between originating meaning and consequential execution.

A system is aligned when its actions remain traceable to the intent, mandate, authority, values and constraints that justified those actions in the first place.

A system becomes misaligned when execution continues but its relationship to originating purpose becomes unclear, weakened, distorted or lost.

This distinction matters.

A model can be behaviourally aligned in a narrow interaction while the broader system remains operationally misaligned.

A workflow can be procedurally compliant while its execution no longer reflects current intent.

An organisation can approve actions through formal process while no longer being able to reconstruct how meaning, authority, context and consequence were connected at the point of execution.

Coherence is the condition produced when alignment is sustained over time.

A coherent system is one in which meaning, execution, evidence and consequence remain mutually consistent across changing contexts.

Alignment is therefore the architectural discipline.

Coherence is the system property it preserves.

The Alignment Architecture is designed to make this relationship explicit.

The Alignment Problem

Historically, meaning, execution and governance have been managed as separate disciplines.

Meaning has been expressed through:

• strategy;
• policy;
• mandate;
• purpose;
• values;
• culture;
• identity;
• institutional obligation.

Execution has been managed through:

• operating models;
• projects;
• workflows;
• processes;
• task management;
• automation;
• AI-assisted orchestration.

Governance has been managed through:

• controls;
• permissions;
• risk frameworks;
• compliance;
• audit;
• accountability structures;
• assurance mechanisms.

In practice, these domains rarely remain synchronised.

Strategy changes, but workflows remain unchanged.

Authority is delegated, but runtime systems do not fully encode the limits of that delegation.

Policy is approved, but execution environments rely on partial interpretations.

AI agents infer next actions, but their relationship to organisational mandate may be unclear.

Automation accelerates, but the meaning behind action becomes increasingly indirect.

Over time, systems experience drift.

Actions continue.

Metrics improve.

Processes become more efficient.

Automation expands.

Yet the underlying intent becomes progressively less visible.

The result is not simply operational inefficiency.

The result is architectural misalignment.

When misalignment persists, coherence breaks down.

Systems may still function, but their actions, evidence, authority and consequences no longer form a reliable whole.

The Alignment Architecture

The Alignment Architecture consists of three interacting layers.

Layer 1 - Meaning

Meaning answers:

Why does this matter?

Layer 2 - Execution

Execution answers:

What should happen next?

Layer 3 - Admissibility

Admissibility answers:

Is this action allowed to happen now?

Together, these layers form a complete architecture for preserving purpose while enabling intelligent action.

Meaning

↓

Execution

↓

Admissibility

↓

Coherence

The Alignment Architecture connects originating meaning to consequential execution through an admissibility boundary. Coherence is preserved when meaning, action, evidence and consequence remain mutually consistent over time.

The three layers are not merely conceptual categories.

They represent different architectural responsibilities.

Meaning orients.

Execution moves.

Admissibility governs consequence.

Coherence emerges when these responsibilities remain connected over time.

A trustworthy AI-mediated system requires all three.

The Alignment Architecture is therefore not a standalone product layer.

It is an organising architecture that can be implemented through semantic models, authority graphs, policy structures, execution passports, admissibility controls, evidence trails and runtime gates.

Layer One: Meaning

The Meaning Layer preserves intent.

In architectural terms, meaning is not sentiment, branding or aspiration.

Meaning is the encoded source of orientation.

It may include:

• purpose;
• mandate;
• identity;
• values;
• commitments;
• strategic objectives;
• transformation intent;
• policy intent;
• legislative obligation;
• delegated authority;
• operating principles;
• safety constraints.

Meaning provides continuity across time and context.

It explains why an action matters, what it is intended to serve, and what boundaries should constrain it.

Meaning does not execute.

Meaning provides orientation.

Without meaning, systems become optimisation engines.

They may maximise outputs, increase efficiency, reduce friction and improve measurable performance while losing sight of why those outputs matter.

At an institutional level, meaning may be represented through:

• legislation;
• board mandates;
• organisational purpose;
• enterprise strategy;
• policy intent;
• regulatory obligation;
• delegated authority;
• customer commitments;
• social licence.

At a personal level, meaning may be represented through:

• identity;
• values;
• aspirations;
• commitments;
• habits;
• health goals;
• life direction.

The architectural role is the same in both cases.

Meaning provides the foundation upon which action is interpreted.

It is the source from which coherent action becomes possible.

Layer Two: Execution

The Execution Layer converts intent into activity.

It is the operational layer of the architecture.

Execution includes:

• plans;
• tasks;
• workflows;
• decisions;
• approvals;
• recommendations;
• interventions;
• routines;
• transactions;
• system changes;
• automated processes;
• AI-generated next actions.

Execution transforms possibility into consequence.

The central architectural primitive of this layer is the Execution Unit.

An Execution Unit is the smallest unit of proposed action capable of producing consequence.

An Execution Unit may be:

• a task block;
• a workflow step;
• an approval;
• a recommendation;
• a payment;
• a message;
• a data access request;
• a customer intervention;
• a system change;
• a generated instruction;
• an operational decision;
• an automated action.

Execution Units are important because they allow alignment to be evaluated at the level where action becomes specific.

High-level intent is not enough.

General approval is not enough.

Broad strategy is not enough.

Alignment must be preserved at the level where a specific action is proposed in a specific context with a specific consequence surface.

Execution creates movement.

However, execution alone cannot determine whether movement is appropriate.

Nor can it determine whether the system remains coherent.

That requires a separate layer.

Layer Three: Admissibility

The Admissibility Layer governs consequence.

It evaluates whether a proposed Execution Unit should be permitted to occur at a specific moment in a specific context.

Admissibility considers:

• authority;
• permission;
• context;
• state;
• safety;
• timing;
• policy;
• trust;
• evidence;
• accountability;
• escalation conditions;
• regulatory constraints;
• operational boundaries;
• consequence severity.

The critical question is:

Is this action admissible at the moment it becomes real?

Admissibility operates at the execution boundary.

It is not the same as strategy.

It is not the same as policy.

It is not the same as workflow.

It is not the same as model alignment.

It is the architectural control function that determines whether proposed action may become institutional, operational or personal consequence.

Examples include:

• Is this AI recommendation appropriate in this customer context?
• Is this transaction authorised now?
• Is this information permitted to be shared with this actor?
• Is this workflow escalation justified by current evidence?
• Is this professional authorised to access this data?
• Is this automated action consistent with policy, mandate and delegated authority?
• Is this system change allowed to proceed under the current operational state?
• Is this personal intervention consistent with the user’s stated goals and constraints?

Admissibility ensures that execution remains bounded by meaning.

Its purpose is not to prevent action.

Its purpose is to ensure that action remains explainable, authorised, contextually appropriate and accountable.

Admissibility is therefore the control boundary through which coherence is protected.

The Alignment Loop

The three layers operate as a continuous cycle.

The basic loop is:

Meaning -> Proposed Execution -> Admissibility Check -> Consequence -> Evidence -> Reflection -> Updated Meaning

This loop allows systems to learn without losing coherence.

In this context, coherence means that the system can adapt, execute and learn without losing its relationship to originating intent.

Meaning provides orientation.

Execution proposes action.

Admissibility evaluates whether action may proceed.

Consequence occurs only when action is permitted.

Evidence records what happened.

Reflection determines whether the action remained aligned.

Meaning is then reaffirmed, refined or updated.

In mature systems, admissibility does not sit only at the end of the process.

It operates across transitions.

It asks whether meaning has been correctly interpreted.

It asks whether the proposed action reflects the current state.

It asks whether authority is present.

It asks whether evidence is sufficient.

It asks whether the action can be explained after the fact.

It asks whether the consequence is within permitted bounds.

This creates a system capable of adaptation without uncontrolled drift.

It also creates a system capable of coherent learning: learning that updates action without severing action from purpose.

AI-Mediated Systems

The rise of AI intensifies the alignment problem.

AI increasingly operates between meaning and execution.

It can:

• generate plans;
• prioritise actions;
• summarise context;
• recommend interventions;
• infer intent;
• coordinate workflows;
• draft communications;
• classify risk;
• influence decisions;
• trigger operational responses.

As AI becomes more operational, the risk of alignment drift increases.

A system may become highly effective at producing outputs while becoming progressively disconnected from the purpose, authority or constraints that should govern those outputs.

This is especially important in AI-mediated environments because cognition is no longer located only in clearly identifiable human decision-makers.

Cognition may be distributed across:

• prompts;
• models;
• retrieval systems;
• orchestration layers;
• memory stores;
• semantic layers;
• workflow engines;
• agents;
• policy engines;
• dashboards;
• summaries;
• human reviewers.

In these environments, alignment cannot be assumed simply because a human remains somewhere in the process.

Nor can it be assumed because a policy exists.

Nor can it be assumed because a model has been tested.

The architectural question is whether the proposed action remains connected to valid meaning, valid authority, valid context and valid evidence at the moment it becomes consequential.

The Alignment Architecture provides a control framework for this environment.

Meaning constrains intent.

Execution performs work.

Admissibility governs consequence.

Coherence is preserved when these remain connected through changing states, actors, systems and decisions.

This creates a bounded form of intelligent execution that remains accountable to its originating purpose.

Human Scale and Institutional Scale

One of the most important properties of the Alignment Architecture is its scale independence.

The same pattern applies to individuals, teams, enterprises, digital platforms and institutions.

The structure remains consistent.

Only the context changes.

Human Scale

At human scale, the Meaning Layer may include:

• values;
• identity;
• personal goals;
• health intentions;
• commitments;
• boundaries;
• life direction.

The Execution Layer may include:

• plans;
• routines;
• habits;
• task blocks;
• recommendations;
• personal workflows;
• AI-assisted planning.

The Admissibility Layer may include:

• consent;
• safety;
• appropriateness;
• trust;
• timing;
• permissions;
• personal constraints;
• emotional or physical readiness.

In this context, the architecture supports coherent personal action.

It helps ensure that activity remains connected to purpose.

Institutional Scale

At institutional scale, the Meaning Layer may include:

• strategy;
• mandate;
• legislation;
• policy intent;
• regulatory obligation;
• operating model;
• delegated authority;
• customer commitments.

The Execution Layer may include:

• business processes;
• operational workflows;
• decision systems;
• approvals;
• transactions;
• AI agents;
• automation platforms;
• enterprise applications.

The Admissibility Layer may include:

• governance controls;
• access rules;
• authority checks;
• policy constraints;
• risk thresholds;
• accountability requirements;
• auditability;
• execution assurance.

In this context, the architecture supports coherent institutional action.

It helps ensure that operational systems remain connected to legitimate authority and accountable purpose.

At both scales, coherence is not a vague ideal.

It is the observable condition in which meaning, action, evidence and consequence continue to make sense together.

Research Lineage: Codex Resonance

The Alignment Architecture is informed by Codex Resonance’s research into semantic coherence, meaning, lineage, trust and constraint in intelligent systems.

Codex Resonance studies how meaning remains stable, interpretable and accountable as information, decisions and actions move across data, AI, organisations and human decision-making.

Arqua applies this lineage to consequence-bearing institutional execution.

Where Codex Resonance addresses coherence as a semantic and architectural problem, Arqua operationalises coherence through execution admissibility, Architecture of Record, Execution Passports, SCIA runtime and the T=0 Convergence Gate.

In this sense, the Alignment Architecture acts as the bridge between Codex Resonance and Arqua.

It preserves Codex Resonance’s concern with coherence while expressing it in enterprise-safe architectural terms.

The relationship can be stated simply:

Codex Resonance asks:

How does meaning remain coherent?

Alignment Architecture asks:

How does meaning become action without losing coherence?

Arqua asks:

Is this consequential action admissible now?

The Alignment Architecture is therefore the translation layer between Codex Resonance and Arqua.

It converts the Codex concern with coherence into an enterprise architecture for admissible execution.

Codex Resonance → Alignment Architecture → Arqua

Codex Resonance:

Meaning, coherence, lineage, trust, constraint

Alignment Architecture:

Meaning → Execution → Admissibility → Coherence

Arqua:

EAA, SCIA, Architecture of Record, Execution Passport, T=0 Convergence Gate

The Alignment Architecture translates Codex Resonance’s concern with coherence into Arqua’s enterprise architecture for admissible execution.

Relationship to Arqua

The Alignment Architecture complements Arqua’s work on Execution Admissibility Architecture.

The Alignment Architecture describes the broader system pattern.

Execution Admissibility Architecture focuses on the consequence boundary within that pattern.

Execution Admissibility Architecture asks:

Is this action admissible at T=0?

The Alignment Architecture expands the frame.

It explains why admissibility matters.

It shows how consequential action must remain connected to originating meaning, operational context and accountable authority.

Together, the two architectures provide a coherent model for trustworthy AI-mediated systems.

Alignment Architecture explains the full cycle.

Execution Admissibility Architecture governs the critical boundary.

SCIA provides a runtime pattern for evaluating state, context and integrity.

Architecture of Record preserves the structural truth required for trustworthy assessment.

Execution Passports carry evidence across the execution pathway.

The T=0 Convergence Gate determines whether proposed action can bind.

Coherence is the condition preserved when these elements remain connected over time.

Relationship to Human-Scale and Transformation Platforms

The Alignment Architecture is not limited to enterprise governance.

It can also describe human-scale and platform-scale systems.

A human-scale Living Codex can be understood as an application of the Alignment Architecture in which:

• Meaning is expressed through values, identity, personal direction and commitments;
• Execution is expressed through routines, task blocks, plans and behaviour;
• Admissibility is expressed through safety, trust, consent, timing and coherence.

In this framing, Living Codex is not positioned as wellness technology.

It is a human-scale reference implementation of meaning-to-execution alignment.

Similarly, transformation platforms, coaching systems, learning platforms and behaviour-change environments can be understood as platform-scale applications of the same architecture.

In that context:

• Meaning is expressed through transformation goals, customer intent, learning objectives and behavioural commitments;
• Execution is expressed through plans, tasks, nudges, workflows and interventions;
• Admissibility is expressed through appropriateness, permission, trust, user context and consequence boundaries.

This creates a clean bridge between personal transformation systems, enterprise execution systems and AI-mediated platforms.

The architecture remains the same.

The implementation context changes.

Coherence provides the connecting principle across those contexts.

At human scale, coherence means personal action remains connected to identity, values and intention.

At platform scale, coherence means user journeys, recommendations and interventions remain connected to legitimate purpose and trusted context.

At institutional scale, coherence means operational systems remain connected to mandate, authority, evidence and accountability.

Boundary Note

This paper does not propose that coherence can be achieved through values statements, ethical principles or AI model tuning alone.

Nor does it argue that alignment is merely a human aspiration or organisational slogan.

The argument is architectural.

Coherence must be supported by structured meaning, governed execution, admissibility controls and evidence-preserving feedback loops.

Alignment becomes real when the relationship between meaning, authority, context, proposed action and consequence can be represented, tested, governed and reviewed.

This is especially important in AI-mediated environments where cognition, recommendation and execution may be distributed across models, tools, workflows, agents and human reviewers.

The Alignment Architecture therefore does not replace AI safety, governance, compliance or enterprise architecture.

It provides a way to connect them around the moment where action becomes consequence.

Why This Matters

The next generation of digital systems will not be judged solely by intelligence, automation or efficiency.

They will be judged by their ability to remain aligned.

They will also be judged by their ability to remain coherent as they adapt.

This matters because many modern systems are becoming execution-rich and meaning-poor.

They can generate action faster than institutions can reconstruct intent.

They can automate workflows faster than governance can assess context.

They can optimise outputs faster than humans can determine whether those outputs remain appropriate.

They can create operational consequence before authority, evidence and accountability have converged.

The result is a new class of architectural risk.

Not merely system failure.

Not merely model error.

Not merely process non-compliance.

The deeper risk is the separation of action from the meaning that should govern it.

Where this separation persists, coherence collapses.

The system may continue to operate, but it becomes harder to determine whether its actions remain meaningful, authorised, appropriate or accountable.

The Alignment Architecture addresses this risk by requiring systems to preserve the relationship between:

• why action matters;
• what action is proposed;
• whether action is allowed;
• what consequence occurred;
• what evidence was retained;
• and how the system learns from that consequence.

The purpose of the Alignment Architecture is therefore not only to permit or prevent action.

Its deeper purpose is to preserve coherence: the continuing consistency between why a system acts, what it does, what it permits, what it records and how it learns.

Conclusion

Meaning without execution remains aspiration.

Execution without meaning becomes optimisation.

Execution without admissibility becomes risk.

And systems without coherence become difficult to trust.

The strongest architectures therefore combine all three.

Meaning preserves purpose.

Execution creates change.

Admissibility governs consequence.

Alignment connects them.

Coherence is the condition sustained when that connection holds over time.

This is the Alignment Architecture.

As AI becomes increasingly involved in human, organisational and institutional decision-making, alignment will become a foundational architectural concern rather than an optional design consideration.

The future of trustworthy systems depends not only on what they can do.

It depends on whether they remain connected to why they were created to act.

In AI-mediated environments, this connection cannot be left implicit.

It must be architected.

The Alignment Architecture establishes the bridge between semantic coherence and execution admissibility.

It explains how Codex Resonance’s research concern with meaning, lineage and coherence becomes Arqua’s enterprise architecture for admissible action.

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