01Foundation
Governance
Principles, decision rights, controls, accountability, evidence, and editorial integrity.
Enterprise outcomeMake responsibility and risk decisions visible.Inspect component
Framework
One connected system for putting enterprise AI into practice.
EAINE brings governance, knowledge, engineering, architecture, delivery, controls, reuse, and adoption together so teams can see and improve the whole system.
Cross-cutting assuranceEvidence + human accountability across every layerEnterprise intent remains traceable to engineering decisions
- Layer 01Foundation
Clear authority and a shared language
01GovernanceFoundation02EBOKFoundation - Layer 02Operating core
Turn strategy into controlled, practical work
03Engineering ModelOperating model - Layer 03Delivery system
Manage delivery, platforms, and controls
04AI SDLCLifecycle05Reference ArchitecturePlatform06StandardsAssurance - Layer 04Scale + learning
Reuse what works and keep improving
07PatternsReuse08PlaybooksAdoption
Operational signalsControlled learningUpdates governance, knowledge, standards, patterns, and playbooks↺
Eight components
Each part solves a clear problem, and all eight work together.
Organisations can adopt EAINE in stages. The connected design keeps governance, architecture, delivery, and operations aligned.
02Foundation
EBOK
A common set of definitions, knowledge areas, concepts, and terms so business, engineering, governance, and assurance teams mean the same thing.
Enterprise outcomeGive every role a common engineering vocabulary.Inspect component
03Operating model
Engineering Model
Capabilities connecting strategy, delivery, platform, governance, and learning.
Enterprise outcomeTurn AI ambition into an enterprise operating system of work.Inspect component
04Lifecycle
AI SDLC
Nine stages that connect the initial decision, design, build, evaluation, release, operation, and improvement of an AI system.
Enterprise outcomeEngineer AI behaviour through traceable lifecycle evidence.Inspect component
05Platform
Reference Architecture
Control points for knowledge, models, agents, tools, identity, evidence, and observability.
Enterprise outcomeCreate reusable platform paths without hiding accountability.Inspect component
06Assurance
Standards
Minimum expectations for AI SDLC, governance, evaluation, security, prompts, agents, and operations.
Enterprise outcomeSet a consistent floor while allowing risk-based depth.Inspect component
07Reuse
Patterns
Repeatable approaches, tradeoffs, failure modes, and anti-patterns.
Enterprise outcomeConvert repeated experience into reusable engineering knowledge.Inspect component
08Adoption
Playbooks
Practical implementation paths, review questions, templates, and 90-day guidance.
Enterprise outcomeHelp teams start with control and learn through delivery.Inspect component
EAINE does not add governance after engineering. It makes accountability, evidence, and learning part of how engineering is performed.
Five traces · five questions to keep connected
Follow one important outcome from the original intent to what happens in operation.
EAINE calls this an operating record. It helps a reviewer understand what was intended, how the system was configured, who decided, what was released, and what was learned after release.
Intent
What outcome matters, for whom, and within which constraints?
Authority
Who may decide, approve, act, stop, and answer?
Behaviour
Which exact configuration produced the outcome?
Evidence
What justified release, and what remains unknown?
Learning
What did production change for the next decision?
Choose safeguards from the actual use
Start with how AI is used and what could happen if it fails.
Software does not become AI-native simply because AI helped build it. Answer these four practical questions first; the framework references remain available for consistent implementation.
How AI helps build the system
Record whether people work without AI, receive AI assistance, accept AI-produced work, or allow AI to execute engineering actions. EAINE records this technically as E0–E4.
What AI does when the system is live
Record whether the delivered AI informs, recommends, prepares an action, or executes an action. EAINE records this technically as R0–R4.
Which real-world situations apply
Add the safeguards needed for internal AI, embedded AI, high-impact decisions, agents, custom models, and external providers. More than one situation can apply.
How serious failure could be
Start with the most serious credible consequence and exposure. Critical factors cannot be hidden by averaging them with lower-risk factors.
System connections
The real value comes from connecting the parts.
Strategy to delivery
Business outcomes and risk appetite become use-case decisions, requirements, architecture, and measurable release criteria.
Governance to evidence
Policies become engineering controls with owners, artefacts, gates, and operational signals.
Platform to practice
Shared capabilities support approved delivery paths without removing team or human accountability.
Operations to learning
Real behaviour, incidents, cost, and feedback update tests, systems, standards, patterns, and investment.
For implementers and reviewers
Trace each idea to the book, website guidance, and governed controls.
The First Edition manuscript is mapped to EAINE’s framework and reference architecture (Framework baseline: EAINE v1.0 · Released 20 October 2026). Verification of the latest revision remains in progress.
Framework baseline: EAINE v1.0Officially Released 20 October 2026
Most readers can continue without these identifiers. Open this reference when you need to implement, review, or cite the framework precisely.
Open the book and control reference map
The First Edition manuscript is mapped to EAINE’s framework and reference architecture (Framework baseline: EAINE v1.0 · Released 20 October 2026). Verification of the latest revision remains in progress.
First Edition, 2026 · ISBN 978-93-345-4606-4 · Framework baseline: EAINE v1.0 · Released 20 October 2026. Review snapshot: 19 September 2026, covering the 7 September manuscript revision. Chapter-lineage and claim reconciliation remain open. The mappings below are navigation references, not verified alignment or independent review evidence.
Framework as operating model
Book anchor: Part I + Chapter 13, Architecture as a Control System
Website anchor: /framework
Control references: DOM-002, DOM-007, DOM-010, and lifecycle stage linkage in controls JSON.
AI engineering platform and governance controls
Book anchor: Chapter 14, 15, 18
Website anchor: /ai-sdlc
Control references: CMP-002, CMP-005, CMP-006, CMP-007, CMP-008, IF-004 to IF-007.
Agent, tool, and decision boundaries
Book anchor: Chapters 9, 20
Website anchor: /evidence
Control references: CMP-006, CMP-010, CMP-011, IF-005, DEC-014, DEC-018.
Knowledge and context supply chain
Book anchor: Chapter 19, Knowledge and Context Architecture
Website anchor: /library
Control references: DOM-004, CMP-007, IF-004, REC-004, REC-005.
Operations and continuous improvement
Book anchor: Chapters 16, 17, 22
Website anchor: /adoption
Control references: DOM-009, DOM-010, CMP-012, CMP-013, CMP-015, REC-015, REC-016.
Role alignment
One shared model, with a clear view for every role.
Everyone works from the same system while focusing on the decisions they own.
| Role | Primary question | EAINE starting point |
|---|---|---|
| CTO / CIO | How do we scale AI value without creating invisible enterprise risk? | Executive brief, maturity profile, 90-day adoption path |
| Engineering Leader | How do teams deliver faster while preserving quality and accountability? | AI SDLC, minimum standards, team evidence model |
| Enterprise Architect | Which shared capabilities and control points should the platform provide? | Reference architecture, traceability matrix, engineering model |
| Security / Risk Leader | Where are AI risks controlled and how can the organisation prove it? | Applicability and risk standards, security standard, evidence traceability |
| AI Engineer | What does good engineering look like beyond a working demonstration? | Lifecycle stages, evaluation, prompt/context, agent, and observability standards |
| Practitioner / Reviewer | How can I apply, challenge, and strengthen the discipline? | Worked examples, templates, patterns, review guide |