agent-native-audit

This tool audits your codebase against agent-native architecture principles by spawning eight parallel sub-agents that evaluate data stores, tools, and prompts, producing a scored report of gaps and strengths.

Agent-native architecture principles include Action Parity, Tools as Primitives, Context Injection, Shared Workspace, CRUD Completeness, UI Integration, Capability Discovery, and Prompt-Native Features, evaluated concurrently across frontend, backend, and tooling.

Yes, this architecture audit applies to AI-enabled projects with plugin-based architectures and multi-agent workflows, evaluating frontend, backend, and tooling concurrently across eight principles.

Parallel code review scoring works by spawning eight sub-agents that simultaneously enumerate artifacts across frontend, backend, and tooling, producing per-principle scores and a final consolidated summary report.

The best way is to run a parallel audit that scores Context Injection and Capability Discovery alongside six other principles, providing concrete recommendations to guide improvements in your AI architecture.

No, you do not need external dependencies to generate a scored architecture report. The audit loads baseline patterns and spawns internal sub-agents to evaluate your codebase and compile a consensus summary.