agent-native-audit

Agent-native architecture ensures AI agents can effectively interact with software through principles like action parity and context injection. An audit analyzes your codebase against these principles, generating a scored report with prioritized recommendations to improve AI agent integration and compliance.

You can audit agent-native architecture by running an automated review that analyzes your codebase against principles like CRUD completeness, UI integration, capability discovery, and prompt-native features. The audit generates a scored assessment with actionable recommendations for each principle.

Yes, an agent-native architecture audit specifically checks for tools as primitives and context injection alongside action parity, shared workspace, CRUD completeness, UI integration, capability discovery, and prompt-native features, providing a scored assessment for each principle.

The best way to evaluate AI agent implementation compliance is to conduct a comprehensive architecture audit that scores adherence to agent-native principles. This automated review identifies gaps in capability discovery and prompt-native features, providing prioritized recommendations for improvement.

Yes, you can use an agent-native audit to improve action parity and shared workspace design. The audit analyzes these specific architecture principles within your codebase and provides a scored report with prioritized recommendations to enhance agent-native compliance.

An agent-native architecture audit relies on analyzing your codebase structure against predefined principles. If capability discovery gaps are not found, ensure your codebase exposes discoverable interfaces and the audit covers all relevant directories for prompt-native features and UI integration.