Runloop, an infrastructure startup, has raised $7 million in seed funding to address what its founders call the “production gap” — the critical challenge of deploying AI coding agents beyond experimental prototypes into real-world enterprise environments. Runloop’s platform addresses a fundamental question that has emerged as AI coding tools proliferate: where do AI agents actually run when they need to perform complex, multi-step coding tasks? For Runloop, the answer lies in providing the infrastructure layer that makes AI agents as easy to deploy and manage as traditional software applications — turning the vision of digital employees from prototype to production reality. Runloop’s core product, called “devboxes,” provides isolated, cloud-based development environments where AI agents can safely execute code with full filesystem and build tool access. These environments are ephemeral — they can be spun up and torn down dynamically based on demand. One customer example illustrates the platform’s utility: a company that builds AI agents to automatically write unit tests for improving code coverage. When they detect production issues in their customers’ systems, they deploy thousands of devboxes simultaneously to analyze code repositories and generate comprehensive test suites. Despite only launching billing in March and self-service signup in May, Runloop has achieved significant momentum. The company reports “a few dozen customers,” including Series A companies and major model laboratories, with customer growth exceeding 200% and revenue growth exceeding 100% since March. Runloop’s second major product, Public Benchmarks, addresses another critical need: standardized testing for AI coding agents. Traditional AI evaluation focuses on single interactions between users and language models. Runloop’s approach is fundamentally different.