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GlossaryProductsIntermediate

CI/CD pipelines

Also written: CI/CD, Continuous Integration/Continuous Deployment, Continuous Integration and Continuous Delivery, Continuous Integration/Continuous Delivery

Automated software development processes that build, test, and deploy code changes frequently and reliably.

In plain terms

CI/CD pipelines are automated workflows that help software developers build, test, and release new features or updates more quickly and reliably.

Think of it like an assembly line for software. When a developer makes a change to the code, the CI/CD pipeline automatically checks that change, tests it to make sure it works, and then deploys it to users. This speeds up the process and reduces the chance of errors compared to doing everything manually.

For example, a company might use a CI/CD pipeline to automatically test a new version of its app every time a developer checks in code, and then automatically release that update to a small group of users to catch any problems early.

The expert version

Continuous Integration (CI) and Continuous Deployment/Delivery (CD) pipelines are automated processes designed to increase the speed and reliability of software development. CI involves frequently merging code changes from multiple developers into a central repository, followed by automated builds and tests. CD extends this by automatically deploying validated code changes to production or staging environments.

These pipelines typically consist of stages such as code checkout, compilation, unit testing, integration testing, and deployment. They are crucial for enabling agile development methodologies, allowing for rapid iteration and faster delivery of value to end-users. Tradeoffs can include the complexity of setting up and maintaining the pipeline, and the potential for rapid propagation of bugs if testing stages are insufficient.

Why it matters now

CI/CD pipelines are becoming more integrated with AI development tools. Recent items show AI assisting in building and debugging these pipelines, and extending their capabilities to manage AI-specific workflows like prompt transpilation. This suggests a trend towards automating the deployment and management of AI-powered applications.

Where it shows up 6 items, newest first
How each company uses it
Google Gemini
Google Gemini is integrating CI/CD concepts into AI agent development, using modular prompt transpilation to build scalable agents and IDE-driven deployments for fixing pipelines.
Microsoft Copilot
Microsoft Copilot is enhancing CI/CD processes with Azure Developer CLI extensions and integrating them with tools like Chaos Studio for testing application resilience.
OpenAI ChatGPT
OpenAI's Codex is being used by companies like Rakuten to speed up software issue fixes, implying its integration into development and deployment workflows.
Related terms

Continuous IntegrationContinuous DeploymentDevOpsSoftware Development LifecycleAutomated TestingGitOps

Often confused with
DevOps
A broader culture and set of practices that emphasizes collaboration and
Agile Development
A project management methodology focused on iterative develop
Infrastructure as Code (IaC)
A practice of managing and provisioning infrastruc
Origin

The concepts of Continuous Integration and Continuous Deployment originated in the software development community in the late 1990s and early 2000s. Jez Humble and David Farley's book 'Continuous Delivery' (2010) is often cited as a foundational text that popularized and formalized these practices. Source ↗

Try this about 5 minutes

You can explore CI/CD concepts by looking at the GitHub Actions marketplace or Azure DevOps documentation. Many open-source projects use these platforms to automate their builds and tests, allowing you to see real-world examples of pipeline configurations. For a hands-on experience, try setting up a simple GitHub Actions workflow for a small code repository to automatically run a linter or a basic test whenever you push changes.

Try it: Playbook entries that use thisPlaybook →

The Playbook: reusable how-tos, prompts, configs and tips pulled out of the news.

Questions people ask

What's the difference between Continuous Delivery and Continuous Deployment?
Continuous Delivery ensures that code changes are always ready to be deployed to production with a manual approval step. Continuous Deployment automatically deploys every validated change to production without human intervention.
Why are CI/CD pipelines important for AI development?
AI development involves frequent experimentation and model updates. CI/CD pipelines automate the testing and deployment of these changes, allowing AI teams to iterate faster and deliver new capabilities more reliably.
Can CI/CD pipelines handle large AI models?
Yes, CI/CD pipelines can be configured to manage large models, but they require robust infrastructure for storage, testing, and deployment, often involving specialized tools and cloud resources.

First seen on Subvolts Mar 11, 2026. Updated Oct 16, 2026. Written by an AI model from the items above and reviewed against them weekly; report an error.