This guide explores Continuous AI, a powerful paradigm for improving software projects by using autonomous AI agents for background tasks. We will look at how to move beyond interactive AI assistance and delegate recurring tasks like code reviews and documentation updates to automated workflows. By examining two practical, real-world examples, you will learn how to implement safe, effective, and autonomous AI jobs that enhance code quality and boost developer productivity without requiring constant human supervision.
Beyond the IDE: The Power of Asynchronous AI Agents
Much of the current focus in AI-assisted software development is on interactive tools — AI coding agents in an IDE or tasks delegated to remote agents that still require direct initiation and oversight. A more transformative approach, however, lies in automating these processes to run as background jobs, independent of human intervention.
This is the core idea behind Continuous AI, a concept championed by the GitHub Next team. It extends the principles of Continuous Integration (CI) by introducing scheduled or triggered jobs that leverage AI to perform complex software development tasks.
Why is this so promising?
Enhanced Productivity: By taking the developer out of the loop for routine tasks, these automated processes free up valuable time for more complex problem-solving. The AI performs the work autonomously, and the developer only engages with the results.
Systematized Best Practices: Continuous AI allows you to codify and enforce team standards and processes directly into your development infrastructure. Instead of relying on manual checks, you can build automated agents to ensure code quality, documentation freshness, and more.
Safe, Controlled Automation: A primary concern with automation is the risk of unintended or destructive changes. In Continuous AI we might addresses this by favoring models that avoid mutating and distructive operations and require an explicit gate to modify the project. The AI agent’s output is often a report or a pull request — artifacts that a human can review, assess, and act upon, maintaining full control over the codebase.
Continuous AI in Action: Two Experiments
To make these concepts concrete, let’s explore two experiments implemented in Ruler, an open-source project. These examples demonstrate how you can achieve added value with relatively simple setups using familiar tools like GitHub Actions.
Experiment 1: "This Codebase Smells!"
The goal of this experiment is to get a regular, high-level health check of the entire codebase, identifying potential "code smells," areas for refactoring, or architectural improvements.
In "This Code Smells!", GitHub Actions and Codex CLI deliver a weekly roasting of the codebase. Constructive, and meant to be taken personally.
How it Works: A GitHub Actions workflow runs automatically every Sunday afternoon. It uses Codex CLI and
GPT-5in unattended mode to perform a comprehensive code review powered by a large language model.The Prompt: The AI's behavior is guided by a clear, version-controlled prompt stored in the repository. The prompt instructs the model to look for general areas of improvement and to format its findings with a touch of humor.
The Output: The agent generates a detailed report and posts it as a new GitHub Discussion.
The Value: This process is entirely autonomous and non-mutating. It requires no human action to run, and its output is a safe, informative report. Project maintainers can review the suggestions at their convenience and create issues or pull requests for the ones they find valuable. This provides a consistent, automated mechanism for identifying potential improvements that might otherwise be missed.
Experiment 2: WRITEME
Project documentation like the README.md file, often falls out of sync with the codebase as new features are added and changes are made. The "WRITEME" experiment automates the tedious task of keeping them aligned.
As projects evolve, documentation drifts. AI can help a lot with updating documentation, but we wouldn't want to merge a change without a review.
writeme.ymlruns periodically and triggers @GitHubCopilot SWE Agent with the task of reviewing the codebase and updating the documentation. The agent creates a PR for the human to review later.
How it Works: A GitHub Actions workflow runs twice a week. It triggers the GitHub Copilot SWE Agent, a cloud-hosted agent designed for complex asynchronous development tasks.
The Prompt: As with the first experiment, a dedicated prompt instructs the agent to analyze the current codebase, compare it against the
README.md, and identify any undocumented features or outdated information.The Output: The agent creates a pull request containing the proposed documentation changes.
The Value: This is a perfect example of a "human-after-the-loop" pattern. The AI performs the time-consuming analysis and drafting work, but a human developer retains ultimate control. They can review the changes, request modifications, or merge the PR as is. This ensures both automation efficiency and code quality, turning a tedious chore into a simple review process.
⚡ Moving from “vibe coding“ and interactive tooling to autonous and asynchronous processes is one of the greatest opportunities for real quality and productivity boosts from AI. It requires a more deliberate approach, and some new techniques, but is an important addition to the toolbox of developers and engineering leads moving to an AI-native Software Development Lifecycle.
Our course, Elite AI-Assisted Coding covers these techniques in depth. Join us for a 3 week (12 live sessions) in-depth study of everything you need to build production-grade software with AI.
Your Turn to Automate
These two experiments, though simple, demonstrate the immense potential of Continuous AI. By building autonomous agents that operate safely in the background, you can systematically improve your project's quality and your team's efficiency.
The key is to start with processes that are safe and add immediate value. Begin with non-mutating, report-generating tasks or adopt the human-after-the-loop model by having your agents create pull requests. By integrating these automated AI workflows into your existing CI/CD pipelines, you can begin to unlock a new level of productivity and engineering excellence.