Evaluating Latency and Cost Tradeoffs for Agentic Search
How to decide between a fast, cheap search call and a deeper, slower one inside an agent loop.
How it works
Search providers and endpoints differ along a real tradeoff curve: a basic organic-results call is typically fast and cheap, while a deeper call (fetching full page content, running a summarization pass, or hitting a 'neural'/semantic search endpoint) costs more in both latency and per-call price. An agent loop's design should map query type to the right point on that curve rather than always using the same endpoint for every search.
Example
A chat agent answering a quick factual question during a live conversation should use the fast, cheap organic-results endpoint with a tight timeout (under a second or two), while a background research agent compiling a report can afford a slower, deeper call with full-page retrieval since no user is waiting synchronously on that specific call.
Pitfalls
- Defaulting every search call to the deepest, most expensive option 'just in case' is a common early-stage mistake that becomes an expensive habit once usage scales.
- Not setting a per-call timeout means one slow search call can stall an entire synchronous agent turn, even if the rest of the pipeline is fast.
- Cost-per-call numbers alone are misleading without factoring in retry costs — an aggressive timeout that triggers frequent retries can end up costing more than a slightly longer initial timeout would have.