For years, Big Tech CEOs have touted visions of AI agents that can autonomously use software applications to complete tasks for people. But take today’s consumer AI agents out for a spin, whether it’s OpenAI’s ChatGPT Agent or Perplexity’s Comet , and you’ll quickly realize how limited the technology still is. Making AI agents more robust may take a new set of techniques that the industry is still discovering.

One of those techniques is carefully simulating workspaces where agents can be trained on multi-step tasks — known as reinforcement learning (RL) environments. Much like labeled datasets powered the last wave of AI, RL environments are starting to look like a critical element in the development of agents.

AI researchers, founders, and investors tell TechCrunch that leading AI

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