Architecture Principles

Guiding principles for enterprise architecture decisions

AI Agents Must Be Auditable

Active

AI Governance

Every AI agent decision must produce an audit trail that explains the reasoning, inputs, and outputs.

Rationale

Regulatory compliance and organizational trust require explainable AI decisions.

Implications

•Implement logging for all agent actions
•Store decision context
•Enable replay capability

Human-in-the-Loop by Default

Active

AI Governance

AI agents require human approval for decisions above defined risk thresholds.

Rationale

Critical decisions need human oversight to manage risk and ensure accountability.

Implications

•Define risk thresholds per domain
•Implement approval workflows
•Track override patterns

Cloud-Native First

Active

Technology

New applications should be designed for cloud deployment using containerization and managed services.

Rationale

Cloud-native design enables scalability, resilience, and operational efficiency.

Implications

•Use Kubernetes for orchestration
•Prefer managed services
•Design for horizontal scaling

Data is a Strategic Asset

Active

Data

Data should be managed as a valuable organizational asset with appropriate governance.

Rationale

Data drives AI capabilities and business insights.

Implications

•Implement data cataloging
•Define data ownership
•Establish quality standards

Security by Design

Active

Security

Security controls must be built into systems from the beginning, not added later.

Rationale

Retrofitting security is costly and often incomplete.

Implications

•Threat modeling in design phase
•Security reviews for all changes
•Automated security testing

API-First Integration

Active

Architecture

Systems should expose capabilities through well-designed APIs as the primary integration method.

Rationale

APIs enable loose coupling, reusability, and ecosystem participation.

Implications

•Document all APIs
•Version APIs properly
•Implement API gateway