2026-04-16 7 min read Every agent needs search: Coding agents search millions of files across repos, or support agents search customer tickets and internal docs. The use cases are different, but the underlying problem is the same: get the right information to the model at the right time. If you're building search yourself, you need a vector index, an indexing pipeline that parses and chunks your documents, and something to keep the index up to date when your data changes. If you also need keyword search, that's a separate index and fusion logic on top. And if each of your agents needs its own searchable context, you're setting all of that up per agent.  AI Search (formerly AutoRAG ) is the plug-and-play search primitive you need. You can dynamically create instances, give it your data, and search — from a Worker, the Agents SDK, or Wrangler CLI. Here's what we're shipping: Hybrid search . Enable both semantic and keyword matching in the same query.…