Patronus AI says it has raised a $50 million Series B to expand its work on agent testing and simulation, alongside a preview of what it calls a digital world model for AI agent training.

The round was led by Greenfield Partners, with participation from Lightspeed Venture Partners, Notable Capital, Datadog, Samsung, Gokul Rajaram and others. TechCrunch reported that the financing brings Patronus AI's total funding to $70 million.

The company started with evaluation tools for language models, including benchmarks and reliability testing products. Its newer pitch is that agent developers need more than static evals: they need controlled environments where agents can operate, fail, recover and be measured before they are put in front of real customers or production systems.

Patronus describes its digital world model as a way to simulate websites and internal systems for agent training and reinforcement learning. TechCrunch reported that the company uses replicas of websites and enterprise software so agents can be stress-tested after training, with successful task completion rewarded and errors penalized.

That target is timely because enterprise agents increasingly need to click through tools, call APIs, move data and make sequential decisions. A model that answers a prompt correctly can still fail when it has to navigate a real workflow with permissions, changing state and ambiguous UI.

The funding does not prove Patronus has solved that problem, and simulated environments can miss production edge cases. But it shows investor and customer demand for a more formal testing layer around agents before companies let them take action in live systems.