Your AI agents are most productive when they have access to real infra. Give them faster feedback loops with automated MCP-enabled environments, and let them test, review, QA, and iterate autonomously.
Writing code is only part of an agent's job. They also need to test, validate, and iterate on their code quickly to stay productive.
✓As soon as your agent pushes code, it can run tests against a real Shipyard environment, manually QA the changes, and apply any fixes (all on its own).
When your team goes home at the end of the day, your AI agents might be stuck waiting for an environment to test their code.
✓Shipyard's on-demand environments give your agents 24/7 access to production-like infra, so they can keep iterating when you're AFK.
Your AI agent has the context of your codebase, and logs from your local environment. But things get increasingly complex when you factor in dependencies, integrations, and stateful data.
✓Let your agents see the full context of your app, including real services and real data. That way, they can catch any edge/corner cases that surface after your local environment.
Your AI agent completes a feature or fix and opens a pull request
Shipyard automatically builds and deploys a full-stack preview environment built just for that branch
Your AI agent runs tests against the environment using the Shipyard MCP, and manually visits it for visual QA
The agent can iterate on fixes and improvements. It can evaluate the new code changes in the env after it rebuilds on each commit
Once the agent's work is complete and the pull request gets merged, the environment automatically spins down
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