Run your own agentic RAG or trust a research API?
can work by giving an AI agent a search tool, a page-reading tool, and clear instructions. The agent searches, reads, checks what it found, searches again, and then combines the evidence into an answer. Running this yourself means you control the loop, the prompt, the model, and .
Research APIs from companies such as Exa, Parallel, and Tavily may be doing a similar behind the scenes, with extra controls added on top. The main question is whether those controls are valuable enough to hand the whole to a black box that cannot be inspected or tuned for a specific field. The hardest parts to copy quickly are the data layer: a strong open-web index, avoiding blocks at scale, and clean that keeps menus and footers out of the context.
above that may be better owned directly if control matters.
Key points
- uses repeated search, reading, checking, and synthesis to build an answer.
- Running it yourself gives control over the prompt, model, loop, and .
- Research APIs can be convenient, but they may hide the inside a black box.
- The difficult part to reproduce is the data layer: web index, scale, and clean .
- Cleaner context can reduce wasted tokens and improve agent cost control.