How are you handling aggregation/counting questions in doc-aware agents? RAG keeps failing me here

Something I keep hitting building agents that work over documents, curious how others solve it. RAG is the default doc tool we give agents, and it's great for "find/explain the passage about X" — the answer lives in one place, retrieval finds it. But the questions users actually ask are often aggregations over the whole collection: "how many invoices are unpaid", "total billed to this client this year", "which contracts expire in the next 90 days". Top-k retrieval structurally can't answer those: * Aggregation is a scan over every record; retrieval hands the agent k chunks. So it computes the answer over a sample, not the population. Ask for a total over 1,000 docs with k=10 and it literally sums 10 of them — then states it with full confidence. * On homogeneous collections the ranking is meaningless anyway: 1,000 invoices are all roughly equidistant from "total unpaid", so which k come back is basically arbitrary. * Raising k doesn't fix it — to be correct k must equal N, i.e. dump the whole corpus into context. Doesn't scale, and a bigger/better model doesn't help because the ceiling is set at the retrieval step before the model sees anything. The approaches I've seen / tried: (a) function-calling to text-to-SQL over a structured table, (b) pre-extracting fields into a metadata store and querying that, (c) just accepting RAG is the wrong tool for these and routing aggregation intents elsewhere. For those of you shipping doc agents in production: what's your actual pattern for counting/aggregation? Do you classify the query intent first and route, or have you found a retrieval setup that genuinely handles it? (Sorry for AI written text but it's only beacause i make mistakes in English)