RAG for decision models is here! Parallel just launched the Decisions API, which combines live web search with bounded decision-making in a single call. Decision models like Jev are useful when the output is small and structured: yes/no, one option from a list, a category, or a routing decision. The problem is that those decisions are only as good as the context you give them. If the answer depends on something that changed recently, or information that only exists on the web, the normal flow becomes retrieval first and decision second. Parallel brings those two steps together. You provide the state and the questions you want answered. The API searches the web, grounds the decision in what it finds, and returns the typed answer along with probability, confidence, and sources. That makes it much more useful for agent workflows where a decision depends on current external information, research tasks where the model needs to verify something before choosing an outcome, or eval pipelines where the judgment needs grounding from the web. It can also answer multiple questions against the same retrieved context in one request, instead of repeating the search for every decision. So instead of: Search → retrieve context → decision model → structured answer you can make the grounded decision directly through one API. I think this is a pretty natural extension of where decision models are going: bounded outputs are useful, but giving that decision layer access to the live web makes the set of problems it can handle much larger.