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Runloop, a San Francisco-based 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.
The funding round, led by The General Partnership with participation from Blank Ventures, comes as the artificial intelligence code tools market is projected to reach $30.1 billion by 2032, growing at a compound annual growth rate of 27.1%, according to multiple industry reports. The investment signals growing investor confidence in infrastructure plays that enable AI agents to work at enterprise scale.
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?
“I think long term the dream is that for every employee at every big company, there’s maybe five or 10 different digital employees, or AI agents that are helping those people do their jobs,” explained Jonathan Wall, Runloop’s co-founder and CEO, in an exclusive interview with VentureBeat. Wall previously co-founded Google Wallet and later founded fintech startup Index, which Stripe acquired.
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The analogy Wall uses is telling: “If you think about hiring a new employee at your average tech company, your first day on the job, they’re like, ‘Okay, here’s your laptop, here’s your email address, here are your credentials. Here’s how you sign into GitHub.’ You probably spend your first day setting that environment up.”
That same principle applies to AI agents, Wall argues. “If you expect these AI agents to be able to do the kinds of things people are doing, they’re going to need all the same tools. They’re going to need their own work environment.”
Runloop focused initially on the coding vertical based on a strategic insight about the nature of programming languages versus natural language. “Coding languages are far narrower and stricter than something like English,” Wall explained. “They have very strict syntax. They’re very pattern driven. These are things LLMs are really good at.”
More importantly, …