Caching and Rate-Limit Strategies for Agent Loops
Practical patterns for not burning your search API quota on repeated or looping agent queries.
How it works
An agent loop can easily issue the same or near-identical search query multiple times within a single task (retrying after a parsing failure, or two sub-agents independently searching the same term), so a caching layer keyed on a normalized query string — lowercased, whitespace-trimmed, common synonyms collapsed — sits between the agent's tool call and the actual API request, serving a recent cached response instead of a redundant live call.
Example
A cache with a short TTL (say, 10 minutes for volatile topics, a day for stable reference queries) stores {normalized_query: response} pairs; when the agent calls search with "tesla stock price" twice in the same task minutes apart, the second call is served from cache with zero additional API cost.
Pitfalls
- A TTL that's too long for a volatile query (stock prices, breaking news) serves confidently stale data that the agent has no way of knowing is outdated.
- Caching on the exact query string misses near-duplicate queries (different phrasing, same intent) that a normalized or semantic cache key would catch, leaving significant redundant-call savings on the table.
- An agent loop that hits a rate limit mid-task needs explicit backoff-and-retry logic — without it, a 429 response can be misinterpreted by the agent as 'no results found' and silently degrade the final answer.