What CI/CD setup do I need for an AI-assisted codebase?
Tests, type checking, and a build on every pull request, plus a preview deployment. When an agent writes most of the code, the pipeline is the reviewer that never gets tired or agrees with you.
Surveyed 17 September 2026
Automate delivery
Open in Tools →- 01
- 02Vercel
Frontend cloud platform offering zero-config deploys, preview URLs, and edge functions.
Developer Tools - 03GitLab CI
Built-in CI/CD for GitLab with shared runners and YAML pipeline configuration, on GitLab.com or self-hosted.
Developer Tools - 04CircleCI
Mature CI/CD platform with Docker, machine, and macOS executors plus reusable orbs and matrix builds.
Developer Tools - 05Buildkite
CI/CD pipelines hosted in the cloud while build agents run on your own infrastructure, suited to large monorepos.
Developer Tools - 06Jenkins
Self-hosted open-source automation server for CI/CD, run on your own infrastructure with a huge plugin ecosystem.
Developer Tools - 07Harness
Meta-skill that designs domain-specific agent teams and defines the specialized agents inside them.
Developer Tools - 08Argo CD
Open-source GitOps continuous delivery controller for Kubernetes, maintained under the CNCF.
Developer Tools - 09HCP Terraform
HashiCorp's hosted Terraform with remote state, runs, policy enforcement, and a private module registry.
Developer Tools - 10TeamCity
CI/CD server from JetBrains that can be self-hosted or run as a managed cloud service.
Developer Tools
A curated selection in editorial order. Use the fit and evidence to judge it for your task. Something missing?
What to look for
- 01Does it block the merge on failure, or only report? Advisory checks get ignored within a week.
- 02Is it fast enough to run on every push? A ten-minute pipeline stops being run.
- 03Can an agent read the failure output and fix it, or is the log unusable?
Common questions
- What should run in CI for an AI-written codebase?
- Type checking, tests, a production build, and a preview deploy. The build catches what type checking misses, and the preview catches what neither can see.
- Should agents be allowed to push directly to main?
- No. Branch, open a pull request, let the pipeline gate it. The value of the pipeline is that it applies to work nobody watched being written.
More in Ship and operate
- Interface with your agentsCLI harnesses, IDEs, control planes, desktop apps, multiplexers, and terminals for steering coding agents.
- Deploy an applicationPublish previews and production builds without managing servers.
- Add a backendCombine databases, storage, APIs, and server-side functions.
- Add authenticationImplement accounts, sessions, identity providers, and authorization.
- Accept paymentsAdd subscriptions, checkout, billing, and payment infrastructure.
- Choose data infrastructureCompare databases, object storage, vector search, ORMs, and managed data services.
- Choose compute and GPUsFind cloud compute, serverless runtimes, and GPU providers for training and inference.
- Monitor product and usageCompare error monitoring, observability, product analytics, and web analytics.
- Add email and messagingSend transactional email, notifications, chat, and product messages.
- Protect agent credentialsCompare vaults, short-lived credentials, and egress proxies that keep secrets out of agent context.