Came across DR-DCI via @jobergum's talk at @aiDotEngineer. It's tackling a problem that comes up constantly once you're building agents: how does an agent actually search over a large and distributed set of documents. Traditional RAG has known limits. It chunks documents, embeds them, and hands the agent a handful of disconnected snippets. If the answer got split across a chunk boundary, or needs cross-referencing two documents, the agent has no way to go look further, it only gets the one retrieval pass. The other extreme, the current "grep is all you need" trend, just gives the agent shell tools and lets it search the raw corpus directly. That's more flexible, but it doesn't hold up at scale, full-corpus grep gets slow and starts timing out as the collection grows across distributed physical nodes. DR-DCI (paper by @zhuofengli96475 and team) is a hybrid of the two. BM25 runs first and narrows a huge corpus down to a manageable set of candidate documents. Those relevant docs get pulled into a bounded, sandboxed workspace, and only then does the agent run real grep/cat/find against that smaller set instead of the whole corpus. 71% accuracy vs 63% for raw grep-everything, and about 20x faster. I made some observations of my own on top of the paper, mainly around productionizing this for distributed systems. These include shard locality, tiered caching, caching differences for immutable vs mutable content, fast sandbox setup/teardown for the VFS workspace, and precomputing common queries. This area (RAG + grep hybrids) feels like a real harness engineering lever for inference providers to differentiate on, both on tool-calling benchmarks and in how agents actually perform on real work.