agent-guardrails

You can enforce coding standards for AI agents by implementing pre-commit hooks that block commits containing bypass patterns and hardcoded secrets. This approach uses executable code hooks to validate agent reliability instead of relying on markdown rules.

The best way to prevent hardcoded secrets in AI-generated code is to run secret detection scans that identify hardcoded tokens, keys, and passwords. This validation runs during pre-commit and post-creation stages to ensure project integrity.

Yes, you need bash and git to run these pre-commit checks and secret detection processes. These dependencies are required to execute the code-based rules and validation logic that enforce project standards.

Deployment verification ensures new features are wired into production by validating that components are properly integrated before they are marked as done. This prevents AI agents from bypassing deployment standards and leaving features disconnected.

You can prevent AI agents from duplicating existing functions by running pre-creation checks that show existing modules and functions. Post-creation validation then detects duplicate functions and missing imports to ensure code uniqueness.

Code hooks provide executable enforcement for AI agent reliability by actively blocking invalid commits and detecting secrets, whereas markdown rules are passive and can be bypassed by agents during development and deployment.