Interesting - can you elaborate a little on what you mean by agentic search here?
I would argue that this is still RAG. There's a common misconception (or at least I think it's a misconception) that RAG only counts if you used vector search - I like to expand the definition of RAG to include non-vector search (like Ripgrep in this case), or any other technique where you use Retrieval techniques to Augment the Generation phase.
IR (Information Retrieval) has been around for many decades before vector search become fashionable: https://en.wikipedia.org/wiki/Information_retrieval
Who cares if you used vector search for the retrieval?
The best vector retrieval implementations are already switching to a hybrid between vector and FTS, because it turns out BM25 etc is still a better algorithm for a lot of use-cases.
"Agentic search" makes much less sense to me because the term "agentic" is so incredibly vague.
1. RAG: A simple model looks at the question, pulls up some associated data into the context and hopes that it helps.
2. Self-RAG: The model "intentionally"/agentically triggers a lookup for some topic. This can be via a traditional RAG or just string search, ie. grep.
3. Full Context: Just jam everything in the context window. The model uses its attention mechanism to pick out the parts it needs. Best but most expensive of the three, especially with repeated queries.
Aider uses kind of a hybrid of 2 and 3: you specify files that go in the context, but Aider also uses Tree-Sitter to get a map of the entire codebase, ie. function headers, class definitions etc., that is provided in full. On that basis, the model can then request additional files to be added to the context.
2. Instead lets the agent decide what to bring into context by using tools on the codebase. Since the tools used are fast enough, this gives you effectively "verified answers" so long as the agent didn't screw up its inputs to the tool (which will happen, most likely).