Architecture Templates¶
Purpose¶
These templates support ES-104 — Architecture and Technical Approach.
Use this family to define how the system will be structured before detailed design and implementation begin. ES-104 should make the architecture reviewable: boundaries, components, data and evidence flows, quality attributes, AI controls, trust boundaries, and major decisions should be explicit.
Recommended workflow¶
Planning Readiness Summary
↓
Architecture Overview
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System Context
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Component Model
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Data and Evidence Flow
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Quality Attribute Strategy
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AI-Control Architecture
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Architecture Decision Records
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Architecture Review
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Architecture Readiness Summary
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ES-105 Detailed DesignCopy location¶
Do not edit the reusable templates directly. Copy completed project versions into:
docs/project-workspace/architecture/
Template set¶
Architecture Overview
Summarize architectural purpose, priorities, source evidence, constraints, risks, and open questions.
02System Context
Define system boundaries, actors, external systems, trust boundaries, and operating context.
03Component Model
Define components, responsibilities, interfaces, ownership, and non-responsibilities.
04Data and Evidence Flow
Map data, evidence creation, review, storage, retention, and downstream use.
05Quality Attribute Strategy
Define architectural strategies for security, privacy, reliability, observability, auditability, usability, and maintainability.
06AI-Control Architecture
Define AI boundaries, review points, prohibited behaviors, fallback behavior, and evidence requirements.
07Architecture Decision Records
Record major decisions, alternatives, rationale, consequences, and verification implications.
08Architecture Review
Review traceability, trust boundaries, components, quality strategies, AI controls, risks, and readiness.
09Architecture Readiness Summary
Summarize readiness to begin ES-105 detailed design.
Completion expectations¶
| Question | Evidence |
|---|---|
| What must the architecture accomplish? | architecture_overview.md |
| What is inside and outside the system? | system_context.md |
| What are the major components and responsibilities? | component_model.md |
| How do data and evidence move through the system? | data_and_evidence_flow.md |
| How are quality attributes handled architecturally? | quality_attribute_strategy.md |
| How is AI bounded, reviewed, evidenced, or prohibited? | ai_control_architecture.md |
| What major architectural decisions were made? | architecture_decision_records.md |
| Has the architecture been reviewed? | architecture_review.md |
| Is detailed design ready to begin? | architecture_readiness_summary.md |
Do not confuse architecture with a diagram. Architecture is the set of decisions, boundaries, responsibilities, controls, tradeoffs, evidence flows, and quality strategies that make later design and implementation accountable.
In AI-era systems, architecture is where governance becomes concrete: human oversight, evidence preservation, data boundaries, model boundaries, fallback behavior, observability, and operational control must be designed into the system.