Patronus AI Introduces Generative Simulators, Outlining Adaptive "Practice Worlds" for the Next Wave of AI Agents
Thursday, January 22, 2026
New research describes how simulations can generate fresh tasks, rules, and grading on the fly, enabling rich, adaptive RL environments for today's agents
SAN FRANCISCO, Dec. 17, 2025 /PRNewswire/ -- Patronus AI today announced "Generative Simulators," adaptive simulation environments that can continually create new tasks and scenarios, update the rules of the world in a simulation environment, and evaluate an agent's actions as it learns.
As AI systems increasingly shift from answering questions to carrying out multi-step work, a key challenge has emerged. The static tests and training data we've used for years often don't reflect the dynamic and interactive nature of real-world systems. Agents that look strong on static benchmarks can stumble when requirements change mid-task, when they must use tools correctly, or when they need to stay on track over longer periods of time. Additionally, as agents improve, they can "saturate" fixed environments--leading learning to plateau--whereas generative simulation aims to keep pace by producing new scenarios instead of enumerating them by hand.
Generative simulators conceptually solve for this. The simulator itself can generate the "assignment", the surrounding conditions, and the oversight/checking process, then adapt those based on how the agent behaves. In other words, instead of a fixed set of test questions, it's a living practice world that can keep producing new, relevant challenges and feedback.
Patronus AI also introduced a new concept called Open Recursive Self-Improvement (ORSI): environments where an agent can improve through interaction and feedback over time, without needing a full retraining cycle between attempts.
"Traditional benchmarks measure isolated capabilities, but they miss the interruptions, context switches, and multi-layered decision-making that define actual work," said Anand Kannappan, CEO and Co-founder of Patronus AI. "For agents to perform tasks at human-comparable levels, they need to learn the way humans do - through dynamic, feedback-driven experience that captures real-world nuance."
"When a coding agent can decompose a complex task, handle distractions mid-implementation, coordinate with teammates on priorities, and verify its work - not just solve LeetCode problems - that's when we're seeing true value in engineering. Our RL Environments give foundation model labs and enterprises the training infrastructure to develop agents that don't just perform well on predefined tests, but actually work in the real world," said Rebecca Qian, CTO and Co-founder of Patronus AI.
Generative simulators underpin Patronus AI's RL Environments offerings. These environments are ecologically valid training grounds where agents learn through trial and error in settings that mirror human workflows. Each environment incorporates domain-specific rules, best practices, and verifiable rewards that guide agents toward optimal performance while exposing them to realistic interruptions and multi-step reasoning challenges.
Patronus AI RL Environments are designed for foundation model labs and companies building agents in target domains.
About Patronus AI
Patronus AI develops AI evaluation and optimization to help companies build top-tier AI products confidently. The company was founded by machine learning experts Anand Kannappan and Rebecca Qian. For more information, please visit https://www.patronus.ai/.
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SOURCE Patronus AI
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