Agentic Search vs RAG: Key Differences Engineers Should Know Before Choosing

Agentic search tools and RAG (Retrieval-Augmented Generation) indexes both retrieve text to place into a model prompt, making their core output identical. The critical difference lies upstream: a search tool relies on an external relevance model and is billed per query, while a RAG index uses your own embeddings and chunking at a fixed ingest cost with near-zero per-query expense. Freshness is the main trade-off — search tools excel at surfacing recent information, whereas indexes are better suited for stable, versioned corpora like internal policies. A RAG retriever can be evaluated offline against labelled data, while a search tool's quality depends on the model's ability to form good queries and only surfaces in end-to-end traces. Engineers are advised to route by question type — classifying queries as freshness-dependent or corpus-bound — rather than defaulting to one approach based on preference.