agent-native-audit

Agent-native architecture principles are core design rules, such as action parity, tools as primitives, context injection, and CRUD completeness, that ensure AI agents operate reliably. Validating alignment across frontend, backend, and agent configuration layers eliminates manual, error-prone architectural reviews.

Automated architecture audit scanning works by launching dedicated parallel sub-agents to comprehensively scan your entire codebase for each design principle. This parallel processing ensures full coverage of frontend, backend, and agent configuration layers without requiring manual code inspection.

Reviewing AI agent configuration layers happens alongside frontend and backend code during a full codebase scan. The audit evaluates all layers collectively against the 8 core agent-native principles to deliver a unified, scored compliance report with prioritized recommendations.

The best way to identify architectural weaknesses is running a scored compliance review that checks against agent-native principles. It delivers an actionable report highlighting specific compliance gaps and prioritized recommendations to resolve issues before they impact user experience.

To run a scored compliance review for agent-native architecture, you need to provide access to your AI agent product's codebase. The audit process requires no manual setup or dependencies, directly scanning the frontend, backend, and agent configuration layers to generate the report.