architecture/standards

Architecture decision consistency across AI agents is maintained by enforcing explicit, versioned decisions with documented rationale and defined scope. This ensures all automated and human stakeholders align on project patterns without drift.

An architecture decision document for AI agents must include explicit versioning, rationale, concrete directory structures, and payload formats. It should map technology-specific configuration and source files directly to project requirements.

Standardize directory structure patterns by mandating complete, technology-specific project trees with all configuration, source, test, and build files. Concrete naming conventions and process patterns ensure cross-agent compatibility.

Architecture drift happens when decisions lack explicit documentation and traceability. Applying strict architecture standards during planning ensures decisions are versioned with rationale, reducing ambiguity between requirements and final implementation.

Yes, reference materials like arch-purpose.md are integrated specifically to guide the creation of starter templates. They provide the necessary evidence and validation data to support technology-specific project structures during initial setup.