Deeptune builds the training infrastructure behind capable AI agents, what it calls training gyms: high-fidelity reinforcement learning environments that simulate professional digital workspaces. Inside these simulated environments, agents work through realistic tasks by trial and error, guided by reward systems that push them toward better ways of completing multi-step workflows on real software, from the tools software engineers use to the systems support teams operate.
The insight driving Deeptune is that the bottleneck for agent capability is increasingly data and environments, not just model size. To get agents that can reliably use computers and complete professional work, you need realistic places for them to practice, with faithful simulations of the applications, states, and edge cases they will encounter in production. Deeptune's environments are designed to be high fidelity precisely so that skills learned in simulation transfer to the messy real world, and the company says its environments have contributed to recent advances in agents' computer-use abilities.
This positions Deeptune in the agent infrastructure layer, supplying the reinforcement learning substrate that model developers and agent builders use to train and improve their systems. As the broader reinforcement learning market is projected to grow rapidly, demand for realistic training environments is expected to scale alongside the push for more autonomous, workflow-completing agents.
Deeptune raised a $43 million Series A led by Andreessen Horowitz in March 2026, with participation from 776, Abstract Ventures, Inspired Capital, and prominent angels including OpenAI researcher Noam Brown, Mercor CEO Brendan Foody, and Applied Compute CEO Yash Patil. Its roughly 20-person team includes engineers from Anthropic, Scale AI, Palantir, Hebbia, Glean, and Retool.
For anyone building agents that must operate professional software reliably, Deeptune offers the training grounds, simulated workplaces where agents can fail safely, learn fast, and emerge ready for real work.