Design an Agentic RAG Workflow

An agentic RAG prompt at Scale lands as something like "design the backend for an agent that reads an incoming insurance claim and decides how to route it." A claim arrives as an email with a PDF attached. The agent pulls the facts out of that mess, finds the policy language and prior context that bear on it, reasons about whether the claim is valid, and hands back a decision. Most of what you design has nothing to do with insurance. Any agent workflow ingests, extracts, retrieves, reasons, acts, and decides, so build that pipeline first and add the claim-specific details at the end.

The trap is collapsing everything into one large-model call with the document pasted into the prompt. That works in a demo. It falls apart once a claim outruns the context window, the model invents a policy clause, or a month's token bill arrives. Retrieval shows the model only the few passages that matter. The agent loop swaps one unaccountable leap for several small, checked steps. And some claims are genuinely ambiguous, which is why there's a human-in-the-loop path at all. An automated wrong answer on a payout costs far more than asking a person.

The Scale AI angle

The bar here is a trustworthy decision at a controlled cost. Scale builds enterprise agent software that reads documents and acts on them, and the conversation keeps circling back to whether you can believe the output and afford to run it. The grade turns on whether the agent retrieves the right context, knows when it's unsure, and keeps token spend per document inside a budget as volume grows. So point at the exact places. Show where retrieval quality is enforced, where a confidence threshold sends a claim to a human, and where every model and tool call is metered.