Cost Optimization for High-Volume Agent Queries
Techniques for keeping search API spend under control in agent loops that search often.
What it means
An agent that searches on every turn, or that fans out multiple searches per task without a budget, can rack up per-query costs far faster than a human clicking through a search box ever would. Cost optimization here is mostly about reducing redundant calls (caching, deduplication) and matching call depth to actual need (a cheap organic-results call versus an expensive deep-content-retrieval call) rather than negotiating a better per-unit price.
In practice
An agent that searches for the same company name across three separate sub-tasks in one workflow, uncached, pays for the same query three times; adding a simple in-memory cache keyed on normalized query text for the duration of a single task run eliminates that redundancy with minimal engineering effort.
Tradeoffs
Aggressive caching saves money but risks serving stale data for volatile queries, so cost optimization always trades off against freshness — the right TTL and cache scope depend on how time-sensitive a given query type actually is, and a one-size-fits-all cache policy either overspends or under-serves depending which side of that line a query falls on.