AI ADRs
Decisions on LLM selection, RAG vs. fine-tuning, vector store choice, and agentic framework adoption.
AI ADRs is a core discipline within Architecture Decision Records. It defines how technology systems should be designed, implemented, and governed to achieve reliable, secure, and maintainable outcomes that serve both technical teams and business stakeholders.
Applying AI ADRs standards reduces system failures, accelerates delivery, and provides the governance evidence required by enterprise clients, regulators like BSP, and certification bodies like ISO. Top technology companies (Google, Microsoft, Amazon) treat these standards as competitive differentiators, not compliance overhead.
📖 Detailed Explanation
Architecture Decision Records (ADRs) capture significant architectural decisions with their context, options considered, rationale, and consequences. They are the institutional memory of an architecture practice.
Industry Context: ADRs stored as Markdown files in the application repository (Architecture-as-Code) or in Confluence/Notion.
Relevance to Philippine Financial Services: Organizations operating under BSP supervision must demonstrate mature architecture decision records practices during technology examinations. The BSP Technology Supervision Group evaluates documentation quality, process maturity, and evidence of systematic practice — all of which are addressed by the standards in this section.
Alignment to Global Standards: The practices documented here are aligned to frameworks used by Google, Amazon, Microsoft, and the world's leading consulting firms (McKinsey Digital, Deloitte Technology, Accenture Technology). They represent the current industry consensus on best practices rather than any single vendor's approach.
Engineering Perspective: For engineers, AI ADRs provides concrete patterns and anti-patterns that prevent common mistakes and accelerate development by providing proven solutions to recurring problems. Rather than rediscovering what doesn't work, teams can apply battle-tested approaches with known trade-offs.
Architecture Perspective: For architects, AI ADRs provides the design vocabulary, decision frameworks, and governance artifacts needed to make and communicate complex technical decisions clearly and consistently.
Business Perspective: For business stakeholders, AI ADRs provides assurance that technology investments are aligned to industry standards, reducing the risk of expensive rework, regulatory findings, and system failures that impact customers and revenue.
📈 Architecture Diagram
flowchart LR
A["AI ADRs
Concept"] --> B["Principles
& Standards"]
B --> C["Design
Decisions"]
C --> D["Implementation
Patterns"]
D --> E["Governance
Checkpoints"]
E --> F["Validation
& Evidence"]
F -.->|"Feedback Loop"| A
style A fill:#1e293b,color:#f8fafc
style F fill:#052e16,color:#4ade80
Lifecycle of AI ADRs: from concept through principles, design decisions, implementation patterns, governance checkpoints, and validation — with feedback loops for continuous improvement.
🌎 Real-World Examples
Amazon's engineering culture institutionalized ADRs as '6-pagers' — narrative documents that propose architecture decisions and enumerate alternatives. Every significant technical decision at Amazon requires a written document reviewed by senior engineers and leadership before implementation. Jeff Bezos famously banned PowerPoint in favor of these written documents because they force clearer thinking. AWS's service design documents follow this pattern.
✓ Result: Architectural consistency across 50+ AWS services globally; engineering decisions traceable to original rationale years after original authors have moved on
GitHub's engineering team pioneered the modern ADR format (now standardized as MADR) and open-sourced their ADR tooling. Every significant GitHub infrastructure decision — moving from Resque to Sidekiq, adopting Kafka, migrating to Kubernetes — has a public or internal ADR. Their GitHub blog documents major architecture decisions with full rationale, making GitHub one of the most transparent engineering organizations.
✓ Result: Architecture decisions traceable back to 2011; engineering onboarding time reduced because new engineers can read ADR history to understand 'why' not just 'what'
Spotify uses ADRs as their primary governance mechanism — no heavyweight ARB process. Their 'Tech Radar' (inspired by Thoughtworks) classifies technologies as Adopt/Trial/Assess/Hold. Squads propose ADRs via GitHub pull requests; senior architects review and merge. The lightweight process enables 4,000+ engineers to make autonomous decisions within guardrails.
✓ Result: 1,800+ ADRs across all squads accessible company-wide; architecture consistency maintained without centralized bottlenecks
Ascendion leverages its AI agentic AAVA platform to streamline solutioning across industries like banking, healthcare, and retail through specialized AI Delivery Representatives (ADRs). ADRs act as intelligent, industry-tailored agents within AAVA that interpret business needs, orchestrate workflows, and ensure human-AI collaboration.
✓ Result: In Banking/Finance, ADRs accelerate discovery phases and cloud migrations, cutting costs by 40% and boosting time-to-market by 60% via automated code generation and compliance checks.
🌟 Core Principles
Every aspect of ai adrs must be deliberately designed, not discovered after deployment. Document design decisions as ADRs with explicit rationale.
Apply ai adrs practices consistently across all systems. Inconsistent application creates governance blind spots and makes incident investigation unpredictable.
AI ADRs practices must demonstrably contribute to business outcomes: reduced downtime, faster delivery, lower operational cost, or improved compliance posture.
Quality of ai adrs implementation must be measurable. Define specific metrics and collect evidence continuously — not only at audit or review time.
Standards for ai adrs evolve as technology and threat landscapes change. Schedule quarterly reviews of applicable standards and update practices accordingly.
⚙️ Implementation Steps
Current State Assessment
Document the current state of ai adrs practice: what is implemented, what is missing, what is inconsistent across teams. Use the governance/scorecards section for a structured assessment framework.
Gap Analysis Against Standards
Compare current state against the standards in this section and applicable frameworks (MADR Format — adr.github.io, Architecture Decision Records — Michael Nygard). Prioritize gaps by business impact and remediation effort.
Design the Target State
Define the target ai adrs state: which patterns will be adopted, which anti-patterns eliminated, which governance mechanisms introduced. Express as a time-bound roadmap.
Incremental Implementation
Implement ai adrs improvements incrementally: pilot with one team or system, measure outcomes, refine the approach, then expand. Avoid big-bang transformations.
Validate and Iterate
Measure the impact of implemented changes against defined success criteria. Incorporate lessons learned into the practice standards. Contribute improvements back to this library.
✅ Governance Checkpoints
| Checkpoint | Owner | Gate Criteria | Status |
|---|---|---|---|
| Current State Documented | Solution Architect | AI ADRs current state assessment completed and reviewed | Required |
| Gap Analysis Reviewed | Architecture Review Board | Gap analysis reviewed and prioritization approved | Required |
| Implementation Plan Approved | Enterprise Architect | Target state and roadmap approved by ARB | Required |
| Quality Metrics Defined | Solution Architect | Measurable success criteria defined for ai adrs improvements | Required |
◈ Recommended Patterns
✦ Reference Architecture Adoption
Start from an established reference architecture for ai adrs rather than designing from scratch. Adapt to organizational context rather than rebuilding proven foundations.
✦ Pattern Library Contribution
When your team solves a recurring ai adrs problem with a novel approach, document it as a pattern for the library. This compounds organizational knowledge over time.
✦ Fitness Function Testing
Encode ai adrs standards as automated architectural fitness functions — tests that run in CI/CD and fail builds when standards are violated. This makes governance continuous rather than periodic.
⛔ Anti-Patterns to Avoid
⛔ Standards Theater
Documenting ai adrs standards in architecture policies that no one reads and no one enforces. Standards without automated validation or governance gates are not operational standards.
⛔ Copy-Paste Architecture
Adopting another organization's ai adrs patterns wholesale without adapting to organizational context, team capability, or regulatory environment. Always adapt; never just copy.
🤖 AI Augmentation Extensions
LLM agents analyze design documents against ai adrs standards, generating structured gap reports with cited evidence and suggested remediation approaches.
This section is optimized for vector ingestion into an AI-powered architecture assistant. Semantic search enables architects to retrieve relevant ai adrs guidance through natural language queries.
🔗 Related Sections
📚 References & Further Reading
- MADR Format — adr.github.io↗ adr.github.io
- Architecture Decision Records — Michael Nygard↗ cognitect.com
- RFC Process — IETF↗ IEEE Xplore
- TOGAF Architecture Decision Log↗ opengroup.org
- Documenting Software Architectures — Bass, Clements, Kazman↗ Amazon
- Building Evolutionary Architectures — Ford, Parsons, Kua↗ O'Reilly