Agents don't search like humans. They issue hundreds of queries per session, need structured extraction over snippet relevance, and care more about p95 latency than the perfect top result. Keenable built a search API around those constraints with a 100B+ page proprietary index, SQL-like query interface, and continuous benchmarking against agent-like workloads.
The founders (Amazon AGI web grounding, Yandex search lead) are betting that wrapping existing search APIs won't cut it when agents become the primary consumers of web data. The architecture reveals what changes when you optimize for machine callers instead of human eyeballs.
Why Agent Search Needs Different Plumbing
Human search optimizes for the first three results and tolerates 500ms variance. Agent search runs in tight loops where every query blocks downstream tool calls. The contract shifts:
Query volume: Agents issue 10-100x more queries per task than humans per session
