LlamaIndex

You have the right model, but do you have the right context? For most teams the answer is no — and the reason is retrieval. Teams are split: some threw out their vector databases entirely, grep and file reads only. Others bet everything on embeddings.So which side is right?

Turns out — both are. Semantic search gives agents a fast first pass over large corpora. Grep and file reads give them the precision to verify, dig deeper, and recover when the top-k chunks cut off mid-answer. But stitching grep and semantic search into a single harness is harder than it looks at scale: server-side search across multi-tenant document corpora, index freshness, permission boundaries, and complex file formats that agents can actually navigate (with text, layout, metadata, and page screenshots) rather than hallucinate through. 

We built this harness into LlamaParse Index: semantic search, server-side grep, and file-level navigation in one reasoning loop. Join LlamaIndex Head of Engineering George He for a look under the hood — the architecture decisions, the dead ends, and a live demo of an agent reasoning across multiple indexes on a real enterprise task.

**What we'll cover:**

*   **Why the grep-vs-embeddings debate is a false binary**: what benchmarking both approaches taught us, and why the answer changes with corpus size.
*   **The harness, end to end**: grep, directory listings, and direct file reads as first-class agent tools, composed with hybrid search and reranking; plus multimodal file objects that give agents visual context for tables where text extraction fails.
*   **Live demo**: an agent navigating multiple indexes — search, grep, read, verify, answer.
