Sumanth (@Sumanth_077) “Web search for AI agents will never be the same! For AI agents, the search layer” — TopicDigg

Web search for AI agents will never be the same! For AI agents, the search layer has to do more than retrieving relevant pages. It has to return the context the model can actually use efficiently. Keenable just released 1.0, and it’s now the top-performing web search API for AI agents across the benchmarks that matter. It ranks first on Artificial Analysis for search quality, and first overall on NEEDLE, its open benchmark that continuously reruns on new queries. Keenable owns the full search stack instead of sitting on top of another search API. That gives it control over how results are retrieved, ranked, compressed, and returned to the agent. It also means the team can keep improving the search layer around the queries agents are actually sending through the system. When search results go directly back into the model as context, returning more text is not always better. It increases token usage and can bury the information the model actually needs for the task. Keenable 1.0 introduces dynamic snippets that return more focused context for each query. Keenable reports that this cuts token consumption by 50% on research tasks. It also adds 100M knowledge cards for queries where structured information is more useful than another set of webpages. For AI agents, web search is becoming part of the reasoning stack itself. Retrieval quality, recency, context size, and cost all affect what the agent can do with the information it finds.