How to evaluate web search for AI

Evaluating search for AI agents requires distinguishing among search, which finds relevant web information, extract, which reads known URLs, and research or task APIs, which perform multi-step searching and synthesis for broader objectives. Effective evaluations should measure the complete agent harness, language model, search, and extraction workflow by judging final answers against trusted, production-representative gold data rather than comparing isolated search responses or relying heavily on public benchmarks. The process involves holding the agent configuration constant while testing search providers or modes, allowing agents multiple search turns, configuring each tool according to its documentation, and assessing model-and-search combinations against realistic cost, latency, and quality constraints. Answers are commonly graded with LLM judges using task-appropriate measures such as factual accuracy, recall and precision, field-level correctness, or research rubrics, but judgments should include explanations and be manually audited. Failures should be separated into tool nonuse, provider errors, retrieval misses, and synthesis failures, since only some directly reflect search quality. Results should emphasize end-to-end cost per resolved task, total tool calls, token use, latency, confidence intervals, and transparent, dated configurations, with providers compared as trade-offs on accuracy-versus-cost and accuracy-versus-latency frontiers rather than ranked as universally best.

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