agent-readiness

To assess codebase readiness for AI-agent integration, evaluate software maturity across eight pillars: style, architecture, testing, code quality, documentation, observability, security, and dev environment. This generates a detailed report indicating the status of each pillar and potential remediation steps.

AI-agent readiness is the measure of a codebase's maturity to support heavy AI-agent use. It is needed before deploying an AI-agent to ensure baseline controls are met across architecture, testing, security, and documentation, preventing operational failures during automated tasks.

You can check codebase maturity for AI agents using a read-only assessment that generates a human-readable and machine-readable report without altering the codebase. This evaluates style, architecture, testing, code quality, documentation, observability, security, and dev environment.

You should run a codebase assessment for AI-agent use before deploying an AI-agent, after initial setup to confirm baseline controls, and periodically to monitor progress. This ensures continuous maturity across style, architecture, testing, and security pillars.

The codebase readiness assessment requires no external dependencies or tools to operate. It runs natively using internal scripts to evaluate the eight maturity pillars and outputs a synthesis dashboard indicating status and remediation steps.

The best way to identify security and architecture gaps before deploying an AI agent is to perform an eight-pillar codebase assessment. This generates a synthesis dashboard detailing the status of each pillar and providing specific remediation steps to close the gaps.