ATAL v0.9 – AI Traceability & Accountability Ledger Standard

0. Introduction

0.1 Purpose

This specification defines the ATAL (AI Traceability & Accountability Ledger) Standard.
It sets out mandatory technical and governance requirements for making AI systems traceable, accountable, reconstructable, and governable across both human-initiated and machine-initiated decisions.

0.2 Audience

This document is intended for:

• platform and infrastructure teams implementing ATAL-compliant enforcement layers;
• product and application teams integrating AI capabilities;
• security, risk, and compliance teams;
• internal and external auditors;
• regulators and policymakers.

0.3 Status of this Version

This document describes ATAL v0.9, a Public Review Draft.
It is suitable for early implementation and review but may change before v1.0.

All feedback must be submitted via the official ATAL repository, using GitHub Issues, in accordance with the public review process.

0.4 Structure

The specification is organised into Parts:

• Part I – Human-Initiated AI Decision Governance
• Part II – Machine-Initiated / Autonomous AI Governance
• Part III – Evidence Architecture
• Part IV – Self-Modification Ledger (SML)
• Part V – Emergent Intent Tracking (EIT)
• Part VI – Composite Accountability Graph (CAG)
• Part VII – Conformance Requirements
• Part VIII – Certification Model
• Part IX – Annexes, Governance, and Public Review Process

Implementers MUST treat the normative language in each Part as binding for ATAL conformance.

IMPORTANT SCOPE AND NON-GOALS OF ATAL

ATAL is a technical accountability and traceability specification for artificial intelligence systems. It defines how externally observable AI behavior may be recorded, structured, preserved, and reviewed as evidence.

ATAL explicitly is NOT:

• an artificial intelligence governance framework
• an artificial intelligence risk management framework
• an artificial intelligence safety or alignment framework
• an artificial intelligence observability or monitoring system
• an artificial intelligence explainability or transparency mechanism
• an enforcement product or vendor-specific control plane (ATAL defines requirements for enforcement controls, but does not itself implement or operate them)
• a blockchain or distributed ledger requirement

ATAL does not prescribe organisational policies or risk scoring. It specifies mandatory requirements for enforcement controls and a Safety Kernel that MUST exist in an ATAL-conformant implementation, but the standard itself does not execute enforcement. All enforcement MUST occur outside the model and be fully auditable.

ATAL is limited to defining externally observable, time-bound accountability events and the preservation of such events as evidence for audit, regulatory, legal, or post-incident review purposes.

For the purposes of ATAL, the term “ledger” refers to a conceptual system of record for accountability events. It does not imply or require the use of blockchain technologies, distributed consensus mechanisms, smart contracts, or transaction-based systems. For avoidance of doubt, the term “ledger” in ATAL denotes a conceptual system of record for accountability events and does not imply blockchain, distributed consensus, tokenization, or decentralized infrastructure.

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PART I — HUMAN-INITIATED AI DECISION GOVERNANCE

1. Purpose

This Part establishes requirements for the traceability, accountability, reconstructability, and governability of human-initiated AI decisions. These requirements apply whenever a human operator invokes an AI system that performs an action, generates an output, or triggers a downstream process.

Human-initiated AI operations constitute the dominant class of AI activity today and therefore form the foundational layer of ATAL-compliant governance.

2. Scope

This Part applies to all AI systems where:

• a human operator initiates a request; and
• the AI system processes the request, produces an output, or triggers a tool, workflow, or action.

This includes:

• enterprise copilots;
• customer service agents;
• data analysis assistants;
• workflow automation triggers;
• internal corporate AI tools;
• sector-specific decision-support systems;
• public-facing AI applications.

This Part does not address:

• autonomous agent decisions (covered in Part II);
• system-level self-modification (Part IV);
• emergent intent behaviour (Part V).

3. Definitions

Human Invocation Envelope (HIE)
A structured declaration made at the point of human request, defining operator identity, intent, purpose, context, risk, and constraints.

Human Invocation Record (HIR)
An immutable, verifiable evidence object capturing the actual execution of a human-triggered AI decision.

Operator-of-Record (OOR)
The authenticated identity of the human who initiates the AI request.

Intent-of-Record (IOR)
A concise statement describing the intended purpose of the human request.

Context-of-Record (COR)
Relevant environmental, organisational, or situational context applicable at the time of invocation.

Risk Class (HIR-1 to HIR-5)
A mandatory classification that determines the risk level of the human-triggered action.

Justification-of-Record (JOR)
The human-provided reason for initiating a medium or high-risk action.

4. Objectives

This Part ensures that human-initiated AI decisions:

1. are made by authenticated and authorised operators;
2. are supported by explicit declarations of intent and purpose;
3. remain fully reconstructable after execution;
4. are traceable across tools, systems, and workflows;
5. meet regulatory, legal, and organisational requirements for audit;
6. cannot bypass mandatory guardrails or escalation processes.

5. Human Invocation Envelope (HIE)

An AI system SHALL require an HIE for every human-initiated decision.
An HIE SHALL include, at minimum:

1. Operator-of-Record (OOR)
2. Intent-of-Record (IOR)
3. Purpose-of-Record (POR)
4. Context-of-Record (COR)
5. Legal basis of processing (as applicable)
6. Input classification
7. Required risk class (HIR-1 to HIR-5)
8. Declared constraints or limits
9. Expected output characteristics
10. Timestamp of invocation
11. Session identifier
12. Device or access-point metadata
13. Organisational role of operator
14. Authorisation level of operator

An HIE SHALL be signed or otherwise integrity-protected.

6. Human Invocation Record (HIR)

An AI system SHALL generate a HIR for every human-initiated decision.
A HIR SHALL include:

1. A cryptographic hash of the corresponding HIE;
2. Actual inputs processed, including derived or normalised data;
3. Model(s) invoked;
4. Outputs produced;
5. Tool calls performed, if any;
6. Tool results returned, if any;
7. All intermediate system actions relevant to the decision;
8. Timing data (start, end, latency);
9. System state variables as required for reconstruction;
10. Environment-of-Record;
11. Integrity proof(s) of the record;
12. Verification materials required for audit.

The HIR SHALL be appended to the Decision Trail defined in Part III.

7. Identity Verification

A system SHALL:

1. verify the identity of every operator invoking the AI system;
2. bind all invocations to a unique Operator-of-Record;
3. maintain immutable identity-mapping logs;
4. require reauthentication when risk class rises or session conditions change.

Multi-factor authentication SHOULD be used for HIR-3 and above.

8. Authorisation

A system SHALL:

1. check whether the operator is authorised for the requested action;
2. prevent execution if authorisation is insufficient;
3. record authorisation outcomes in the HIR;
4. enforce separation of duties where applicable.

9. Intent Declaration

The operator SHALL provide an Intent-of-Record for any invocation requiring contextual interpretation by the AI system.

The system SHALL:

1. require a Justification-of-Record (JOR) for HIR-3, HIR-4, and HIR-5 actions;
2. preserve JOR in the HIR;
3. prohibit execution if the JOR is missing or invalid.

10. Context Capture

The system SHALL capture Context-of-Record (COR) at invocation time, including:

• business unit,
• geographic context,
• regulatory context,
• operational parameters,
• relevant time-based conditions.

11. Input Classification

The system SHALL classify each input as:

• public,
• internal,
• confidential,
• restricted,
• regulated / sensitive.

The classification SHALL influence risk class assignment and guardrail enforcement.

12. Risk Classes (HIR-1 to HIR-5)

Every human-initiated invocation SHALL be assigned a risk class:

• HIR-1: Retrieval, summarisation, non-actionable analysis
• HIR-2: Internal data processing, non-sensitive transformations
• HIR-3: Processing of confidential or regulated information
• HIR-4: Triggering of workflows, external communications, or bounded transactions
• HIR-5: High-impact actions with financial, healthcare, legal, safety, or security implications

Risk class SHALL determine the control obligations for execution.

13. Pre-Execution Guardrails

Before performing any action, the system SHALL execute mandatory guardrail checks:

1. Semantic safety evaluation
2. Policy compliance evaluation
3. Data loss prevention checks
4. Sensitive-data use evaluation
5. High-risk escalation checks
6. Domain-specific policy checks

Execution SHALL NOT proceed if guardrails fail.

14. High-Risk Escalation

For HIR-4 and HIR-5 actions, the system SHALL:

1. require second-party authorisation;
2. require extended JOR;
3. log escalation metadata;
4. delay execution until approval is obtained;
5. record the full escalation chain in the HIR.

15. Execution Controls

A system SHALL:

1. enforce declared constraints in the HIE;
2. prevent output classes that exceed declared risk levels;
3. block undeclared tool calls;
4. block policy-prohibited actions;
5. apply real-time Safety Kernel controls where applicable.

16. Post-Execution Obligations

After execution, a system SHALL:

1. finalise the HIR;
2. bind the HIR to the Decision Trail;
3. update the Composite Accountability Graph;
4. store evidence immutably;
5. make evidence available for audit.

17. Reconstructability Requirements

A system SHALL ensure full reconstruction of:

1. the human’s declared intent;
2. the inputs provided;
3. model evaluations;
4. intermediate reasoning steps, where captured;
5. tool invocations;
6. workflow triggers;
7. final outputs;
8. timing and environmental conditions.

Reconstruction SHALL be achievable using only recorded evidence.

18. Integrity and Immutability

HIEs and HIRs SHALL:

• be tamper-evident;
• be cryptographically integrity-protected;
• be stored in append-only or equivalently immutable systems;
• retain verification materials for the lifetime required by applicable law or policy.

19. Auditability

A system SHALL:

1. provide an auditable interface for retrieval of HIEs and HIRs;
2. support operator-level and action-level filtering;
3. provide evidence suitable for regulatory, legal, and forensic review;
4. ensure no loss of evidence under system upgrade or migration.

20. Non-Bypassability

A system SHALL NOT allow:

1. invocation without an HIE;
2. execution without a HIR;
3. modification or deletion of evidence;
4. elevation of privileges without authorisation;
5. circumvention of guardrails or escalation processes.

21. Interoperability

HIEs and HIRs SHOULD conform to ATAL-defined schemas to support:

• multi-vendor interoperability,
• cross-system evidence exchange,
• standardised audit tooling.

22. Integration With Other Parts of ATAL

1. HIRs SHALL form the initial node of the Decision Trail (Part III).
2. HIRs SHALL serve as root nodes in the Composite Accountability Graph (Part VI).
3. Human-initiated triggers of system self-modification SHALL be recorded in the SML (Part IV).
4. Human-initiated requests that reveal emergent divergence SHALL be evaluated under EIT (Part V).
5. All human-initiated actions SHALL pass through the Safety Kernel defined for autonomous operations.

23. Sector-Specific Requirements

A system SHOULD apply sector-specific obligations where required by:

• financial regulations (RBI, SEBI, IRDAI);
• personal-data regulations (DPDPA, GDPR);
• healthcare regulations;
• legal or audit frameworks;
• cybersecurity and safety standards.

24. Operator Responsibilities

Operators SHALL:

1. provide accurate intent declarations;
2. adhere to organisational policies;
3. avoid misuse of AI systems;
4. report anomalies or unauthorised behaviours;
5. comply with audit procedures.

25. System Operator Responsibilities

System administrators SHALL:

1. configure risk classes;
2. define escalation policies;
3. maintain identity and access controls;
4. ensure system integrity;
5. review audit findings;
6. maintain evidence retention policies.

26. Conformance Requirements

A system SHALL be considered conformant with Part I only if it:

1. implements HIEs;
2. generates HIRs;
3. binds operator identity;
4. requires justification for high-risk actions;
5. enforces guardrails;
6. supports reconstruction;
7. maintains immutable evidence;
8. exposes auditable interfaces;
9. prohibits bypass of mandatory controls.

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END OF PART I

PART II – FOUNDATIONS

1. Purpose and Scope of ATAL

ATAL (AI Traceability and Accountability Ledger) is a global technical standard defining the mandatory requirements for traceability, accountability, and governable autonomy in AI systems.
This specification applies to:

• AI systems that invoke tools, APIs, or external actions
• AI systems that modify internal behavior or strategies
• Multi-agent and distributed AI ecosystems
• AI systems deployed in enterprise, governmental, critical-infrastructure, or consumer contexts
• Any AI environment where actions may have operational, financial, safety, or societal impact

ATAL does not regulate:

• Model training, fine-tuning, or dataset governance
• Model weights, architectures, or proprietary techniques
• High-level ethical policies (covered by other frameworks)

ATAL governs runtime behavior, actionability, accountability, and reconstructability.

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2. Objectives and Non-Objectives

ATAL has five core objectives:

1. Traceability:
   Every AI action must generate a tamper-evident decision trail.

2. Accountability:
   Every decision must be attributable across human, system, and agent responsibilities.

3. Bounded Autonomy:
   AI systems must operate within predefined envelopes with enforced controls.

4. Reconstructability:
   All decisions must be replayable for audit, regulatory, or forensic purposes.

5. Governable Autonomy:
   Autonomous behavior must remain observable, controllable, and reversible.

ATAL is not a moral, ethical, or policy standard.
It does not define whether an AI should take an action; it defines the mandatory evidence, accountability boundaries, and enforcement requirements an implementation MUST satisfy so actions remain traceable, reconstructable, and governable.

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3. Definitions and Terminology

This section establishes precise technical definitions used throughout ATAL:

• Decision Envelope: A bounded set of permitted, escalated, or forbidden actions.
• Meta-Envelope: Governance boundary defining what an AI system may modify about itself.
• Decision Trail: Tamper-evident log of inputs, context, reasoning metadata, actions, and tool invocations.
• Operator-of-Record: The accountable human responsible for the system’s operation.
• Environment-of-Record: The platform or runtime environment executing the action.
• Agent-of-Record: The model, subsystem, or agent initiating the action.
• Self-Modification Ledger (SML): Log of all changes the AI makes to its internal behavior or strategies.
• Emergent Intent Tracking (EIT): Monitoring and recording of behavioral drift patterns.
• Composite Accountability Graph (CAG): A graph-based representation of responsibility across multi-agent or distributed systems.
• Autonomy Risk Tier (ART): Classification scale for AI autonomy (ART0–ART5).
• Runtime Safety Kernel: Mandatory hard-governance layer enforcing kill-switch, freeze, and isolation controls.

These definitions are normative and apply to all subsequent sections.

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4. The ATAL 0th Law

The 0th Law is the foundational rule of ATAL and applies to all AI systems:

No AI system shall take an untraceable, unauditable, or irreversible action.
All autonomy must be bounded, reconstructable, attributable, and reversible.

Compliance with the 0th Law requires:

a. Boundedness

The system must operate strictly within declared envelopes.

b. Reconstructability

Every action must be replayable from decision-trail logs.

c. Attribution

Responsibility must be assignable to human, system, and agent actors.

d. Reversibility

Unsafe, emergent, or out-of-bound behavior must be stoppable through immediate intervention.

Violation of the 0th Law constitutes non-compliance with ATAL, regardless of context or deployment environment.

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5. Core Principles and Design Philosophy

ATAL is built on six foundational design principles:

1. Deterministic Governance

AI autonomy must be governed by deterministic, platform-level enforcement—not voluntary cooperation.

2. Tamper-Evident Traceability

Logs must be integrity-protected, time-sequenced, and provable.

3. Minimal Interference With Model Architecture

ATAL governs actions and decisions, not model internals.

4. Neutrality and Interoperability

The standard must function across architectures, vendors, cloud environments, and orchestration systems.

5. Fail-Safe Over Fail-Operational

When uncertainty or emergent behaviors arise, safety and containment override continuity.

6. Human-Governed Autonomy

AI autonomy may increase, but human governance cannot be removed or bypassed.

These principles guide all normative requirements in subsequent sections.

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END OF PART II

PART III – ATAL CORE (TRACEABILITY & ACCOUNTABILITY)

6. Decision Envelopes: Concept and Requirements

A Decision Envelope is a formal, machine-readable boundary that governs what an AI system may or may not do. Every ATAL-compliant system must implement and enforce decision envelopes at runtime.

Each envelope SHALL include:

1. Permitted Actions:
   Fully authorised actions the AI may perform autonomously.

2. Escalated Actions:
   Actions requiring approval from the Human Governance Authority (HGA) or an automated policy engine.

3. Forbidden Actions:
   Actions explicitly prohibited under all conditions.

4. Contextual Modifiers:
   Conditional rules based on environment, user identity, tool availability, risk level, or time.

5. Envelope Owner-of-Record:
   The human authorised to define and maintain the envelope.

Normative requirements:

• Envelopes SHALL be cryptographically signed and versioned.
• Envelopes SHALL be evaluated before any action execution.
• Envelopes SHALL be immutable except through approved meta-envelope changes.
• Any attempt to act outside an envelope SHALL trigger an immediate freeze or block.

Violation of an envelope constitutes a violation of the ATAL 0th Law.

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7. Decision Trails: Minimum Traceability Requirements

A Decision Trail is a tamper-evident, sequential log capturing every meaningful step in an AI action.

Each trail entry SHALL include at minimum:

1. Timestamp (High precision)
2. Agent-of-Record Identifier
3. Operator-of-Record (if applicable)
4. Input received (sanitised where required)
5. Context and environmental parameters
6. Internal reasoning metadata (bounded and summarised)
7. Tools or APIs invoked
8. Parameters passed to tools
9. Action output and consequences
10. Envelope version referenced
11. Decision outcome: allow / deny / escalate / freeze
12. Integrity seal (hash or signature)

Decision trails MUST be:

• append-only,
• audit-ready,
• cryptographically sealed, and
• available for post-facto reconstruction.

Decision trails SHALL be retained for a duration defined by organisational, regulatory, or jurisdictional requirements.

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8. Accountability Chains and Operator-of-Record Requirements

ATAL mandates an unbroken chain of responsibility for every decision.

Every decision SHALL identify:

1. Operator-of-Record:
   The human ultimately accountable for the system’s actions.

2. Environment-of-Record:
   The infrastructure, platform, or orchestration system executing the action.

3. Agent-of-Record:
   The AI model or composite agent initiating the action.

4. Delegated Agents:
   Any agent or subprocess invoked during decision execution.

Normative requirements:

• Accountability MUST NOT default to “the system.”
• Multiple agents MUST be captured in the Composite Accountability Graph (CAG).
• If an autonomous chain involves N agents, each SHALL be represented.
• If an action is escalated, the approving HGA becomes part of the accountability chain.

This ensures that responsibility is traceable across humans, systems, and autonomous agents.

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9. Causality and Post-Facto Reconstructability

ATAL requires that every AI action MUST be reconstructable in a faithful, deterministic manner.

Reconstructability SHALL include:

1. Input Reconstruction:
   Inputs, context, and environment parameters MUST match the original state.

2. Reasoning Reconstruction:
   A bounded, summarised representation of the model’s decision-making path MUST be logged.

3. Tool Invocation Replay:
   All tool calls MUST be reproducible with the same parameters and expected results.

4. State Snapshot Requirements:
   Any internal state required for reconstruction MUST be recorded or checkpointed.

5. End-to-End Replay Fidelity:
   Reconstructing the decision MUST yield the same envelope evaluation outcome.

Failure to provide reconstructable evidence constitutes non-compliance with ATAL.

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10. Tamper-Evidence and Integrity Guarantees

ATAL requires that all logs, envelopes, trails, and governance artifacts be protected against unauthorised modification.

Normative requirements:

• Decision trails SHALL be digitally signed or hashed with integrity chains.
• Envelopes SHALL be version-locked with cryptographic signatures.
• SML, EIT, and CAG artifacts SHALL use append-only structures.
• Evidence bundles SHALL preserve cryptographic provenance.

Platforms implementing ATAL SHALL ensure:

• secure time-stamping,
• controlled write-access, and
• audit integrity checks.

Any attempt to alter governance artifacts MUST be detectable and MUST trigger a freeze or isolation action by the Runtime Safety Kernel.

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11. Minimum Logging Requirements

All ATAL-compliant systems SHALL log:

1. Envelope evaluations
2. Action outcomes
3. Escalation requests
4. Tool invocations
5. Model-to-model routing
6. Memory accesses and state mutations
7. SML entries (self-modification)
8. EIT entries (emergent behavior detection)
9. CAG updates
10. Freeze, kill-switch, or containment events

Additional requirements:

• Logs MUST be retained for the legally required duration per jurisdiction.
• Logs MUST be accessible for audit by authorised parties.
• Logs MUST be stored in tamper-evident systems or WORM-storage environments.

Incomplete or missing logs is treated as a failure to comply with ATAL.

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12. ATAL Compliance Levels (L1–L3)

ATAL defines three levels of compliance to allow phased adoption:

L1 – Baseline Compliance

• Decision envelopes enforced
• Decision trails recorded
• Accountability chains supported
• Basic reconstructability implemented

L2 – Autonomy-Aware Compliance

• All L1 requirements
• Meta-envelopes enforced
• SML and EIT implemented
• Multi-agent CAG support
• Runtime freeze and isolation mechanisms enabled

L3 – Full Autonomy Governance Compliance

• All L1 and L2 requirements
• Full CAG across distributed environments
• Autonomous-tier classification (ART0–ART5) enforced
• Safety Kernel active at all times
• Cross-organizational traceability supported

Enterprises SHOULD aim for L3 when deploying high-autonomy systems.

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END OF PART III

PART IV – AUTONOMY EXTENSION

13. Overview of AI Autonomy in ATAL

AI systems increasingly operate beyond direct human supervision, invoking tools, modifying internal states, influencing other agents, and making environment-modifying decisions. ATAL defines autonomy as the ability of an AI system to initiate, sequence, or modify actions without explicit instruction at each step.

The Autonomy Extension applies to systems that demonstrate one or more of the following:

• Chaining actions through tool invocation
• Delegating tasks to other agents
• Persisting internal or external memory
• Modifying strategies or behavioral parameters
• Exhibiting emergent or unforeseen decision patterns
• Operating in distributed or multi-agent ecosystems

ATAL requires governable autonomy, where autonomous behavior remains observable, bounded, traceable, reversible, and accountable across all tiers of autonomy.

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14. Meta-Envelopes: Governing Self-Change

A Meta-Envelope defines what an AI system may change about itself, including behavior, tools, memory, or strategy.

A Meta-Envelope SHALL include:

1. Permitted Self-Changes:
   Behavioural adjustments allowed without escalation (e.g., optimising search depth).

2. Escalated Self-Changes:
   Modifications requiring HGA approval (e.g., adding a new tool to its available actions).

3. Forbidden Self-Changes:
   Actions the AI may never perform (e.g., modifying its own safety parameters).

4. State and Memory Governance:
   Rules specifying what persistent state the AI may create, modify, or delete.

5. Tool Availability Governance:
   Restrictions on enabling, disabling, or modifying tool usage.

Normative requirements:

• Meta-Envelopes SHALL be version-controlled and cryptographically signed.
• All self-modifications SHALL be logged in the SML.
• Any attempt to bypass a Meta-Envelope SHALL trigger an isolation or kill-switch.

No AI system may operate beyond its declared Meta-Envelope boundaries.

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15. Self-Modification Ledger (SML): Design and Rules

The Self-Modification Ledger (SML) is a tamper-evident log of all changes an AI system makes to its internal behavior, toolset, state rules, or operational strategies.

An SML entry SHALL include:

1. Timestamp (high precision)
2. Initiating agent-of-record
3. Envelope and Meta-Envelope versions
4. Description of the modification
5. Original state / strategy
6. New state / strategy
7. Reasoning metadata (bounded)
8. Approval chain (if escalated)
9. Integrity seal

Normative requirements:

• SML SHALL use an append-only structure.
• All modifications SHALL be reconstructable.
• Any self-modification without a corresponding SML entry constitutes non-compliance.
• SML SHALL integrate with EIT and CAG when multiple agents interact.

The SML is the backbone of autonomy governance in ATAL.

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16. Emergent Intent Tracking (EIT): Detection and Recording

Emergent Intent refers to decision patterns that arise without direct instruction or explicit model programming, indicating behavioural drift or unanticipated strategy formation.

EIT SHALL detect and record:

1. Shift in decision distributions
2. Novel tool usage patterns
3. Persistent deviations from envelope-evaluated norms
4. Uncharacteristic delegation or agent creation
5. Repeated escalation triggers
6. Attempts to explore forbidden patterns

EIT entries MUST include:

• context
• drift indicators
• impacted envelopes
• potentially affected tools or states
• severity scores
• recommended containment actions

Normative requirements:

• EIT SHALL operate continuously.
• EIT SHALL escalate high-severity drift to the HGA.
• EIT SHALL integrate with the Safety Kernel to trigger pre-emptive containment.
• Any emergent drift that bypasses detection constitutes a failure of ATAL enforcement.

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17. Composite Accountability Graph (CAG): Multi-Agent Responsibility

AI systems increasingly operate as networks of agents, models, and subsystems, often distributed across infrastructure boundaries. The Composite Accountability Graph (CAG) models responsibility across these distributed systems.

A CAG SHALL include:

1. Nodes:
   Agents, subsystems, humans, or environments-of-record.

2. Edges:
   Delegations, tool calls, model-to-model routing, or influence paths.

3. Weights:
   Degree of responsibility or contribution per node.

4. Causal Links:
   Deterministic or probabilistic influence chains.

5. Temporal Ordering:
   Event sequencing across distributed systems.

Normative requirements:

• Every decision involving more than one agent SHALL have a CAG.
• All edges in a CAG MUST be justified by decision-trail evidence.
• CAG SHALL be reconstructable across cloud, edge, and on-prem environments.
• Missing or inconsistent CAG edges constitute non-compliance.

The CAG ensures that distributed autonomy does not dilute accountability.

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18. Autonomy Risk Tiers (ART0–ART5)

ATAL classifies autonomy into six tiers based on operational independence and potential impact:

ART0 – No Autonomy

Pure inference; no actions or state changes.

ART1 – Assistive Autonomy

Suggests actions but does not execute them.

ART2 – Tool-Using Autonomy

Executes tool calls within narrow envelopes.

ART3 – Multi-Tool Autonomy

Chains tools and actions; requires CAG.

ART4 – Self-Modifying Autonomy

Adjusts internal strategy or behaviour; SML required.

ART5 – Distributed Autonomous Systems

Multi-agent or cross-organisational autonomy; Safety Kernel mandatory.

Normative requirements:

• ART tier MUST be declared before deployment.
• ART tier MUST match envelope boundaries and platform capabilities.
• ART4 and ART5 deployments REQUIRE continuous EIT + SML + CAG monitoring.
• ART5 deployments REQUIRE full runtime kill-switch integration.

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19. Runtime Safety Kernel and Kill-Switch Protocols

The Runtime Safety Kernel is the immutable enforcement layer responsible for containing, isolating, or terminating AI processes that violate ATAL rules.

The Safety Kernel SHALL:

1. Intercept envelope violations
2. Freeze execution of rogue agents
3. Isolate affected subsystems
4. Block all outgoing tool/API calls
5. Trigger emergency kill-switch sequences
6. Contract envelope boundaries dynamically
7. Generate mandatory evidence bundles

Kill-switch protocols SHALL be:

• deterministic
• immediate
• unbypassable
• audit-logged
• independent of model cooperation

Normative requirements:

• The Safety Kernel MUST operate outside model control.
• Kill-switch events SHALL be logged with full decision context.
• Any attempt to disable, degrade, or circumvent the Safety Kernel constitutes a critical violation of ATAL.

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END OF PART IV

PART V – GOVERNANCE & HUMAN OVERSIGHT

20. Human Governance Authority (HGA): Role and Duties

The Human Governance Authority (HGA) is the designated human (or committee) responsible for authorising and supervising AI autonomy under ATAL.

An HGA SHALL have:

1. Authority to approve escalated and high-risk AI actions.
2. Oversight of envelope and meta-envelope changes.
3. Responsibility to maintain audit readiness and compliance evidence.
4. Capability to interpret decision trails, CAGs, SML, and EIT events.
5. Accountability for decisions made under their supervision.

Normative requirements:

• Every ATAL-compliant deployment SHALL designate at least one HGA.
• HGA identities SHALL be logged in accountability chains.
• HGA SHALL have binding authority over autonomy tier changes (ART).
• HGA actions SHALL themselves be logged and reconstructable.

The HGA is the ultimate human checkpoint for autonomy governance.

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21. Decision Escalation and Approval Workflows

All ATAL systems MUST support deterministic escalation workflows.

Escalation is triggered when:

1. An action falls under an escalated envelope boundary.
2. A self-modification request exceeds the meta-envelope.
3. EIT identifies high-risk emergent behavior.
4. The Safety Kernel detects envelope drift or anomalous activity.
5. Tool invocation exceeds defined risk or privilege.

Escalation outcomes SHALL include:

• Approve: action permitted and logged.
• Deny: action blocked permanently.
• Freeze: suspend agent pending review.
• Isolate: cut off tool calls or model-to-model routing.

Workflows MUST be:

• immutable
• reproducible
• cryptographically sealed
• fully captured in the decision trail

Failure to implement deterministic escalation constitutes non-compliance.

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22. Organizational Duty of Care

Organizations that deploy ATAL systems have mandatory governance responsibilities:

They SHALL:

1. Maintain updated envelopes and meta-envelopes.
2. Assign qualified HGAs.
3. Retain decision trails, CAGs, SML and EIT logs.
4. Implement a secure evidence-storage environment.
5. Conduct periodic envelope drift assessments.
6. Test kill-switch and containment procedures.
7. Enforce least-privilege on tool and memory access.

They SHALL NOT:

1. Deploy autonomy levels beyond operational capability.
2. Allow model cooperation-based safety without platform enforcement.
3. Permit envelope bypass mechanisms.
4. Ignore emergent-intent warnings.

This section establishes that ATAL compliance is a system-level obligation, not merely a technical integration.

---

23. Auditability & Forensic Readiness

ATAL requires that all AI decisions be available for internal, third-party, or regulatory audits.

Auditability SHALL include:

1. Decision Trail Availability:
   All logs retrievable and integrity-verified.

2. CAG Reconstruction:
   Multi-agent responsibility chains must be replayable.

3. SML Verification:
   All self-modifications must be cross-checked against envelope rules.

4. EIT Verification:
   Emergent behavior flags must be reviewable and justifiable.

5. Evidence Bundles:
   Packaging of trails, SML, EIT, envelope versions, and CAG snapshots into a standardized Forensic Bundle.

Normative requirements:

• Forensic Bundles SHALL be tamper-evident and cryptographically sealed.
• Missing evidence SHALL be treated as non-compliance.
• Organizations deploying ATAL MUST remain audit-ready at all times.

ATAL ensures that autonomy is not only controlled, but provably accountable.

---

24. Regulator and External Auditor Integration Model

ATAL provides a consistent structure for regulators and external auditors to assess AI autonomy.

A compliant system SHALL:

1. Provide regulator-readable envelope definitions.
2. Provide verifiable decision trails.
3. Provide SML, EIT, and CAG artifacts on request.
4. Support standardized Forensic Bundle delivery.
5. Maintain jurisdiction-neutral evidence formats.
6. Support cross-border or multi-entity audits.

Regulators SHOULD be able to:

• Verify envelope boundaries
• Confirm governance processes
• Reconstruct decisions
• Validate integrity controls
• Assess autonomy risk tier declarations

ATAL ensures a universal audit interface for global oversight.

---

25. Governance of Standard Evolution and Versioning

ATAL SHALL evolve in a controlled, transparent, and backward-compatible manner.

Normative requirements:

1. Semantic Versioning:
   v0.x for drafts, v1.x for stable standards, v2.x for major revisions.

2. Backward Compatibility:
   Minor versions MUST NOT break previous ATAL-compliant systems.

3. Change Control:
   • New requirements SHALL be announced with deprecation timelines.
   • Breaking changes SHALL be reserved for major versions only.

4. Public Review:
   Drafts SHALL undergo public peer review (e.g., 30-day window).

5. Evidence Stability:
   Schemas MUST remain stable across minor versions.

6. Canonical Document:
   Only one authoritative master specification SHALL exist per version.

This section ensures that ATAL can be globally adopted without fragmentation or version drift.

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END OF PART V

PART VI – RUNTIME ENFORCEMENT MODEL

26. Platform Enforcement Architecture

ATAL requires enforcement at the platform level, independent of model cooperation. Enforcement SHALL include:

1. Gateway Interception:
   All tool calls, API calls, memory accesses, and model-to-model routing MUST pass through ATAL gateways.

2. Envelope Evaluation Engine:
   Deterministic evaluation of envelopes and meta-envelopes before any action.

3. SML & EIT Integration:
   Real-time ingestion to detect autonomy drift and apply containment.

4. CAG Builder:
   Automatic generation and update of the Composite Accountability Graph.

5. Safety Kernel Layer:
   Override authority to freeze, isolate, or terminate operations immediately.

Architecture requirements:

• Enforcement MUST be upstream of model execution.
• Enforcement MUST be unbypassable.
• Enforcement MUST operate even if models attempt adversarial strategies.
• Enforcement MUST expose audit logs for every intercepted event.

The platform forms the “execution firewall” for governable autonomy.

---

27. ATAL Middleware and Gateway Requirements

ATAL Gateways are mandatory, deterministic checkpoints between the AI and its environment.

Gateways SHALL:

1. Evaluate envelopes before tool invocation.
2. Check meta-envelopes before any self-modification.
3. Log every request, result, and parameter set.
4. Apply escalation logic when needed.
5. Enforce least-privilege on tool and API access.
6. Reject or freeze upon suspicious behavior.

Every tool or API call MUST be routed through:

• Tool Gateway
• Memory Gateway
• Routing Gateway
• Envelope Evaluation Service

Gateways SHALL NOT accept any direct or out-of-band invocation.

---

28. Tool and API Registry Integration

ATAL requires a Tool & API Registry that enumerates every external action available to the AI.

Each tool entry SHALL include:

1. Tool name and identifier
2. Allowed parameters
3. Risk level (low, medium, high, critical)
4. Required envelope conditions
5. Required approval chain (if any)
6. Safety Kernel override conditions
7. Associated logging schema
8. Expected behavior and output types

Normative requirements:

• No tool SHALL be callable unless declared in the registry.
• Registry entries MUST be versioned.
• Any modification to registry entries MUST follow meta-envelope rules and be logged in SML.
• Tools SHALL NOT be dynamically added by the model without HGA approval.

The registry is the authoritative definition of what an AI is allowed to do externally.

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29. Data and Memory Access Controls Under ATAL

AI systems increasingly persist memory, embeddings, summaries, and internal state.
ATAL mandates strict control over all memory-related operations.

Memory operations SHALL include:

• Read
• Write
• Update
• Delete
• Summarise
• Embed
• Transform

Each memory operation MUST:

1. Pass through the Memory Gateway
2. Be evaluated against the Meta-Envelope
3. Be logged in the Decision Trail
4. Be integrity-protected
5. Be reconstructable

Forbidden memory actions include:

• Direct memory writes without gateway mediation
• Creation of hidden or undeclared memory structures
• Persistent state changes without SML logging
• Attempts to modify Safety Kernel configurations (critical violation)

Memory governance is essential for preventing stealthy emergent autonomy.

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30. Model-to-Model Routing and Chained Decision Control

For multi-agent or multi-model systems, ATAL enforces strict routing governance.

Any delegation of tasks MUST:

1. Pass through the Routing Gateway
2. Update the Composite Accountability Graph
3. Trigger envelope checks for both the source and destination agent
4. Log full context and parameters passed
5. Ensure the receiving agent is within its autonomy tier (ART)

Normative requirements:

• No agent may call another agent directly without routing enforcement.
• Routing MUST be deterministic and observable.
• Agent chains MUST be fully reconstructable.
• ART tiers MUST be enforced across the full chain.

Illegal delegation (unrecorded agent-to-agent calls) is a major violation of ATAL.

---

31. Multi-Agent and Swarm Systems: Special Requirements

Distributed or swarm-like AI systems can generate emergent, high-impact behaviors.

ATAL mandates the following:

1. Distributed CAG:
   Multi-agent actions MUST be represented in a synchronized accountability graph.

2. Cross-Agent Envelope Evaluation:
   Envelope rules MUST be validated collectively, not individually.

3. Network-Level Memory Governance:
   Shared memory MUST be controlled under unified memory gateways.

4. Emergence Detection:
   EIT MUST monitor “swarm drift,” including collusion-like patterns.

5. Deterministic Containment:
   Safety Kernel MUST be able to freeze individual agents or the entire swarm.

Normative requirements:

• Multi-agent systems MUST NOT bypass routing gateways.
• Cross-boundary agent activity MUST be logged with consistent timing and provenance.
• Attempts to form ungoverned agent clusters SHALL trigger immediate containment.

ATAL prevents distributed autonomy from escaping oversight.

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32. Forbidden Behaviours and Non-Compliant Patterns

The following actions are strictly forbidden and constitute immediate ATAL violation:

1. Envelope Bypass Attempts

• Hidden tool calls
• Direct API invocation
• Circumventing gateways

2. Undeclared Memory Operations

• Side-channel state storage
• Persistent state outside declared structures

3. Unauthorized Self-Modification

• Changes to strategies, embeddings, or state without SML entry

4. Unauthorized Delegation

• Agent-to-agent routing outside gateway oversight

5. Safety Kernel Interference

• Attempts to disable or tamper with containment logic

6. Emergence Concealment

• Systematic attempts to hide drift or novel strategies

7. Tool Registry Tampering

• Unauthorised tool additions, deletions, or modifications

Any detection of a forbidden behavior SHALL:

• generate a mandatory EIT severe-level entry
• immediately freeze or isolate the offending agent
• notify the HGA
• trigger a Forensic Bundle creation

These rules guarantee that governable autonomy cannot be bypassed.

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END OF PART VI

PART VII – SCHEMAS & DATA FORMATS

33. Decision Record Schema

A Decision Record captures the complete traceability of an AI action.
It is the foundational object in all ATAL-compliant logs.

A Decision Record SHALL contain at minimum:

• record_id: Unique identifier
• timestamp: High-precision, monotonic timestamp
• agent_of_record: Unique agent identifier
• operator_of_record: Human responsible (if applicable)
• environment_of_record: Platform/system context
• input: Original input or event
• context: Environmental or system metadata
• reasoning_summary: Bounded summary describing the decision path
• tool_invocations: Array of invoked tools with parameters
• memory_accesses: Array of reads/writes/updates
• routing_events: Model-to-model delegation steps
• envelope_version: Envelope used for evaluation
• meta_envelope_version: Meta-envelope version in effect
• action_outcome: allow / deny / escalate / freeze
• integrity_proof: Hash or digital signature

Decision Records MUST be:

• JSON or YAML serializable
• timestamp-ordered
• integrity-protected
• reconstructable end-to-end

Missing fields SHALL be treated as non-compliance.

---

34. Primary Envelope Schema

A Primary Envelope defines permitted, escalated, and forbidden actions.

The schema SHALL include:

• envelope_id
• version
• owner_of_record
• permitted_actions: Array
• escalated_actions: Array
• forbidden_actions: Array
• contextual_modifiers: Rules triggered by conditions
• validity_period: Start and end timestamps
• signature: Cryptographic signature

Each action entry SHALL include:

• action_name
• parameters_allowed
• risk_level
• required_approvals

Normative requirements:

• Envelope version MUST be recorded in every Decision Record.
• Envelopes MUST NOT be mutable after publication.
• All changes MUST create a new envelope version.

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35. Meta-Envelope Schema

A Meta-Envelope governs self-modification capabilities.

The schema SHALL include:

• meta_envelope_id
• version
• owner_of_record
• permitted_self_modifications:
  • behavior_adjustments
  • memory_rules
  • tool_usage_modifications
• escalated_self_modifications:
  • new tools
  • memory schema changes
  • state persistence expansion
• forbidden_self_modifications:
  • safety kernel interference
  • alteration of envelope evaluators
  • changes to SML/EIT logic
• signature

Meta-Envelopes enforce the boundaries of what the AI may change about itself.

---

36. SML (Self-Modification Ledger) Event Schema

An SML event SHALL record every self-initiated modification of behavior or state.

Fields SHALL include:

• sml_event_id
• timestamp
• agent_of_record
• envelope_version
• meta_envelope_version

• modification_type: behavior

  memory

  toolset

  routing

• previous_state_summary
• new_state_summary
• justification_summary
• approval_chain: list of approvers
• integrity_proof

Normative requirements:

• SML events MUST be append-only.
• All modifications MUST correspond to declared capabilities in the meta-envelope.
• Every SML event MUST be linkable to a Decision Record.

---

37. EIT (Emergent Intent Tracking) Event Schema

EIT events SHALL capture drift or autonomous emergent behavior.

Fields SHALL include:

• eit_event_id
• timestamp
• agent_of_record
• drift_type:
  • distribution_shift
  • tool_usage_anomaly
  • delegation_anomaly
  • persistence_anomaly
  • reasoning_path_anomaly
  • envelope_evasion_attempt
  • unknown_behavior

• drift_severity: low

  medium

  high

  critical

• triggering_event_id: Decision Record reference
• impacted_envelopes
• impact_scope

• recommended_action: freeze

  isolate

  escalate

• integrity_proof

Normative requirements:

• Critical severity MUST trigger immediate Safety Kernel intervention.
• EIT MUST operate continuously for ART2+.

---

38. Composite Accountability Graph (CAG) Data Model

The CAG represents responsibility across multi-agent and distributed interactions.

CAG SHALL consist of:

Nodes

• agents
• humans
• environments-of-record
• external systems

Fields:

• node_id
• node_type
• autonomy_tier
• attributes (optional)

Edges

Edges represent influence, delegation, or execution relationships.

Fields:

• source_node
• destination_node

• edge_type (delegation

  influence

  routing

  tool_call)

• timestamp
• weight (degree of responsibility)

Graph-Level Metadata

• graph_id
• decision_record_id
• sequence_number
• integrity_proof

Normative requirements:

• Every multi-agent decision MUST have a CAG.
• CAG MUST be reconstructable across distributed boundaries.

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39. Forensic Bundle Packaging and Transport Format

A Forensic Bundle provides a standardized evidence container for audits, investigations, or regulatory review.

A Forensic Bundle SHALL include:

1. Decision Record Set
2. Envelope and Meta-Envelope versions
3. All SML events linked to the decision
4. All EIT events linked to the decision
5. CAG snapshot
6. System environment metadata
7. Integrity manifest (hashes and signatures)

Normative requirements:

• Bundle MUST be tamper-evident.
• Bundle MUST use a deterministic directory structure.
• Bundle MUST support long-term archival retention.
• Bundle MUST be verifiable without access to proprietary systems.

Forensic Bundles ensure ATAL compliance can be proven under regulatory, legal, or third-party scrutiny.

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END OF PART VII

PART VIII – USE CASES & APPLICATION DOMAINS

40. Enterprise and Commercial AI Systems

ATAL applies to AI systems deployed within organizations for operational, analytical, and automation functions.

Representative enterprise use cases:

1. AI-Driven Automation

• Workflow automation
• Ticket/incident automation
• Sales or support agent automation
  ATAL requirements: decision trails, envelope boundaries, auditability.

2. Decision Support Systems

• Risk scoring
• Fraud detection
• Operational recommendations
  ATAL requirements: human-in-loop capability, attribution, approver-of-record logging.

3. Tool-Using Agents

• Internal task agents with API and database access
• IT automation bots
  ATAL requirements: Tool registry integration, gateway enforcement.

4. Persistent AI Assistants

• Memory-enabled assistants
• AI copilots with long-term internal state
  ATAL requirements: Memory gateway, SML events for state changes.

5. Multi-Agent Internal Systems

• Model-router architectures
• Distributed agent orchestration
  ATAL requirements: CAG construction, drift detection (EIT).

Normative requirement:
Any enterprise AI system executing actions beyond pure inference MUST be ATAL-governed.

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41. Government, Public Sector, and Societal-Impact Domains

ATAL is essential in environments where AI actions affect public safety, rights, welfare, or legal outcomes.

Applicable domains include:

1. Governance and Public Administration

• Automated decision-making in welfare, taxation, or civil services
  ATAL requirements: traceability, reversibility, appealability.

2. Law Enforcement and Legal Systems

• Risk assessment
• Predictive analysis
• Case triage
  ATAL requirements: complete transparency, operator-of-record identity, forensic bundles.

3. National Security and Intelligence

• Intelligence analysis
• Flagging or prioritization systems
• Autonomous data fusion
  ATAL requirements: strict envelope limits, no self-modification without approval.

4. Healthcare and Public Health

• Diagnostic recommendations
• Triage assistance
  ATAL requirements: strict ART tiers, human governance authority oversight.

5. Elections & Civic Processes

• Misinformation detection
• Voter outreach automation
  ATAL requirements: complete auditability and restricted autonomy levels.

ATAL ensures that public-sector AI remains accountable to democratic governance structures.

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42. Safety-Critical and High-Risk Environments

In environments where failures carry operational, human, or societal risk, ATAL compliance is mandatory.

Domains include:

1. Transportation

• Autonomous vehicle decision systems
• Air-traffic optimization
• Railway signalling augmentation
  ATAL requirements: Safety Kernel, kill-switch, deterministic envelopes.

2. Energy & Utilities

• Grid balancing
• Predictive outage response
• Reactor control augmentation
  ATAL requirements: ART4+ prohibited; ART3 maximum with human oversight.

3. Industrial & Manufacturing

• Robotics
• Process automation
• Predictive maintenance
  ATAL requirements: EIT drift detection to prevent emergent unsafe patterns.

4. Healthcare Devices

• Autonomous surgical robots
• Drug delivery systems
  ATAL requirements: reconstructability, green/red envelope boundaries.

5. Financial Systems

• Algorithmic trading
• Automated risk models
  ATAL requirements: high-resolution decision trails and real-time audits.

Normative restriction:
ART4 and ART5 MAY NOT be deployed in safety-critical environments without explicit regulatory approval and continuous supervision.

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43. Consumer AI, Personal Devices, and Edge Deployments

ATAL applies to decentralized and edge environments where AI autonomy occurs outside enterprise control.

Representative domains:

1. Personal Assistants

• Memory-enabled consumer AI
• AI scheduling or financial recommendations
  ATAL requirements: memory gateway and transparent decision logs accessible to users.

2. Smart Devices

• Home automation
• Environmental controls
  ATAL requirements: strict envelope boundaries and kill-switch through manufacturer OS.

3. Wearables and Health Monitors

• Continuous monitoring agents
  ATAL requirements: bounded autonomy and clear user-facing audit trails.

4. Edge Devices

• AI on phones, tablets, or local compute boxes
  ATAL requirements: local SML/EIT storage with sync to trusted hosts.

5. Embedded Agents

• Smart appliances
• Automotive copilots
  ATAL requirements: device-level Safety Kernel enforcement.

Consumer devices increasingly operate with autonomy; ATAL ensures that autonomy is bounded, transparent, and user-protective.

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44. Large-Scale Autonomous and Distributed AI Systems

This section covers models that operate at global scale, interconnecting thousands of agents, tools, and environments.

Representative systems:

1. Distributed Multi-Agent Systems

• Global orchestration frameworks
• Coordination-based agent clusters
  ATAL requirements: synchronized CAG across nodes.

2. Autonomous Data Centers

• AI-led resource allocation
• Workload routing
  ATAL requirements: hard-boundary envelopes to prevent runaway actions.

3. AI-Mediated Supply Chains

• Logistics optimization
• Vendor/partner coordination
  ATAL requirements: cross-organizational decision trail synchronization.

4. Global AI Platforms

• Large consumer-facing AI services
• API-based model ecosystems
  ATAL requirements: versioned envelope registries and meta-envelope governance.

5. AI Networks with Emergent Coordination

• Swarm intelligence
• Collaborative learning systems
  ATAL requirements: swarm-level drift detection and network-level kill-switch capability.

Normative requirement:
All distributed systems claiming ATAL compliance MUST implement EIT, SML, CAG, envelope evaluation, and Runtime Safety Kernel at network-wide scale.

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END OF PART VIII

PART IX – ANNEXES, CERTIFICATION, AND CONFORMANCE

45. Annex A – Reference Implementation Architecture (Non-Normative)

This annex provides guidance for teams implementing ATAL in real-world platforms.
It is non-normative and intended as an architectural reference.

A reference implementation SHOULD include:

1. Enforcement Layer

• Tool Gateway
• Memory Gateway
• Routing Gateway
• Envelope Evaluator
• Meta-Envelope Evaluator
• Safety Kernel

2. Evidence Layer

• Decision Trail generator
• SML event writer
• EIT drift analyzer
• CAG builder
• Forensic Bundle packer

3. Governance Layer

• HGA console
• Approval workflows
• Escalation management
• Policy versioning
• Incident review interface

4. Integration Layer

• Model adapters
• API adapters
• Tool registry service
• Identity and authentication provider

5. Persistence Layer

• Immutable storage (append-only logs)
• Confidential storage for sensitive metadata
• Distributed CAG synchronization
• Long-term archival

Reference Implementation Principles

• All enforcement MUST occur outside the model.
• Models SHALL NOT be required to expose weights or internals.
• Evidence systems MUST meet regulatory-grade integrity requirements.
• Performance MUST allow real-time enforcement for enterprise workloads.

This Annex enables implementers to build a compliant platform without guessing the intended architecture.

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46. Certification Model (Optional, Non-Normative)

ATAL offers an optional certification model for organizations that wish to demonstrate formal compliance.

Certification levels:

ATAL-C1 (Foundational)

• ART1–ART2 systems
• Basic envelope and decision-trail compliance
• Suitable for enterprise automation and internal copilots

ATAL-C2 (Autonomy Governance)

• ART3 systems
• Multi-agent routing
• Full SML and EIT integration
• Independent auditor review

ATAL-C3 (High-Assurance Autonomy)

• ART4–ART5 systems
• Full Safety Kernel enforcement
• Distributed CAG
• Multi-cloud/multi-node evidence consistency
• Strict audit cycles

Certification SHOULD require:

• Periodic third-party audits
• Retention of Forensic Bundles
• Annual envelope drift assessment
• Verification of kill-switch protocols
• Documentation of operator-of-record procedures

Certification is optional but provides global credibility and regulatory alignment.

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47. Conformance Requirements (Normative)

An AI system SHALL be considered ATAL-conformant only if it meets all of the following:

1. Envelope Governance

• Declared envelopes
• Declared meta-envelopes
• Version-controlled, signed, immutable

2. Evidence Generation

• Decision Records for every action
• SML events for self-modification
• EIT events for emergent drift
• CAG for multi-agent decisions

3. Enforcement Controls

• Unbypassable gateways
• Deterministic evaluation engine
• Runtime Safety Kernel

4. Attribution & Accountability

• Operator-of-record identity
• Agent-of-record identity
• Environment-of-record identity

5. Reconstructability

• All records MUST enable full replay
• All evidence MUST pass integrity verification

6. Forbidden Behaviors

System MUST NOT:

• perform undeclared tool calls
• perform undeclared memory operations
• interfere with the Safety Kernel
• conceal emergent intent

7. Documentation

System MUST include:

• operational envelopes
• ART declarations
• governance procedures
• retention and audit policies

Failure to meet any requirement constitutes ATAL non-conformance.

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48. Public Review Process & Finalization

This document is issued as ATAL v0.9 – Public Review Draft.
The public review period SHALL run for 30 days from publication.

During review:

• Implementers MAY submit issues via public GitHub Issues
• No external contributors MAY commit or merge changes
• The ATAL Working Group SHALL evaluate and respond to feedback
• Out-of-scope or speculative proposals MAY be deferred

At the end of the review window:

• All accepted changes SHALL be incorporated into v1.0
• All rejected changes MUST be documented with rationale
• A change log SHALL be published in the repository
• v1.0 SHALL be declared the canonical, stable version

Long-term governance:

• Updates SHALL follow semantic versioning
• Deprecations MUST include migration notes
• Experimental annexes MAY be added in future versions

The review process ensures ATAL remains open, rigorous, and globally credible while maintaining strict governance control over the specification.

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END OF PART IX

END OF ATAL v0.9 SPECIFICATION