Continue

Continue is an AI-powered quality-control platform designed to enforce engineering standards directly inside the GitHub pull-request workflow. Positioned as “quality control for your software factory,” it allows teams to define custom checks as markdown files stored in source control, then automatically runs those checks on every PR as native GitHub status checks. When code misses the mark, Continue suggests fixes, turning human-defined standards into automated gatekeepers that scale with development velocity.

The tool is built by Continuous AI (continuedev on GitHub) and appears to represent an evolution from an earlier open-source AI coding assistant toward a broader “mission control” for code quality. Rather than offering generic AI review comments, Continue emphasizes consistency and specificity: it only enforces what teams explicitly write into their checks, avoiding unsolicited opinions or surprise bugs. It also integrates with external tools such as Slack, Sentry, and Snyk, allowing checks to pull in context from across the software lifecycle.

At its core, Continue is a specialized CI/CD-adjacent service that uses AI agents to interpret and apply repo-defined rules. Teams create and run these agents through a web interface, with the actual execution priced per token on the Starter tier or bundled into seat-based plans for larger groups. The result is a system that aims to reduce manual review load while keeping humans in control of which standards matter. Because the checks live in the repository alongside the code, they are versioned, diffable, and owned by the engineering team rather than hidden inside a SaaS dashboard.

Getting started with Continue begins by connecting a GitHub repository to the platform and defining checks as markdown files stored directly in the repo. Each check is essentially a standard written in natural language—examples shown on the site include “Anti-Slop,” “Accessibility,” and “Code Security Review”—that tells an AI agent what to look for in the changed code. When a developer opens a pull request, Continue triggers these agents automatically and surfaces the results as native GitHub status checks, complete with pass/fail states and inline suggested fixes where violations are found. Reviewers and authors can then accept or reject suggestions directly within the PR workflow, keeping the entire quality-control process inside familiar GitHub interfaces.

Behind the scenes, Continue uses a pay-as-you-go token model or seat-based credits to power its agents. The platform connects to frontier models and can pull in external context via integrations like Slack, Sentry, and Snyk, meaning a check can reference error logs or security alerts when evaluating code. Teams on the Team or Company plans can manage and share private agents, control which agents are available to different members, and enforce SSO requirements. Because the checks are source-controlled markdown, they follow the same review and versioning practices as the application code itself, making it easy to iterate on standards without leaving the repository.

The workflow is designed to be additive to existing CI/CD pipelines: checks run alongside tests and builds, providing a human-readable layer of policy enforcement that complements automated unit tests. Unlike traditional static analyzers that rely on rigid rule engines, Continue uses large language models to interpret intent, which means teams can express standards in plain English rather than learning a domain-specific query language. However, this also means results are probabilistic, and teams should treat critical checks as assistive rather than infallible until they have been battle-tested on their specific codebase.