rigour from Rigour Labs is an AI-driven governance agent that enforces security and structural standards in developer codebases. It monitors AI-assisted coding sessions and performs continuous scanning for vulnerabilities while evaluating architectural consistency across languages. The tool exposes a live governance dashboard, adaptive drift monitoring, zero-setup Model Context Protocol integration, and terminal-directed alerts for immediate visibility. Its primary audience is developers, DevOps engineers, and security teams who require continuous oversight of AI-generated code in existing workflows.

What tasks can you actually use it for?

The tool scans source code to identify hardcoded credentials, secrets, SQL injection patterns, and cross-site scripting vectors, and it analyzes code complexity and adherence to architectural patterns across major languages. When a vulnerability is flagged the agent generates corrected code and can apply changes with developer approval, moving remediation from a separate ticket back into the codebase so teams address security and architecture issues inside normal coding workflows.

How reliable are its automated fixes?

The agent adapts to project-specific patterns to reduce false positives and refine suggestions, which increases relevance for repetitive, mundane fixes. Generated edits originate from the processing model, so complex or context-sensitive vulnerabilities still need human judgment; the system applies edits only with user approval according to the documentation. Early adopters in the MCP community report immediate visibility and practical value for routine governance activities.

What happens to your code and data during analysis?

The product runs as a local Model Context Protocol server while using AI models for analysis, so files are processed through the local MCP endpoint rather than a separate cloud-only scanner. The developer notes that users should consult the privacy policy for specifics about external model handling or any telemetry. This arrangement keeps integration local to the development environment while still relying on AI-based analysis engines.

A pragmatic governance layer for AI-assisted development

rigour is a practical option for developers and security teams who need continuous oversight of AI-generated code. Expect that some model-generated suggestions require independent verification, particularly for complex or contested security issues. The tool suits teams integrating AI assistants into existing workflows who prefer immediate, code-level guidance rather than separate reporting pipelines.