What is a search agent? How they work and how to build one

Search agents are AI systems that identify user intent, break complex questions into subqueries, retrieve and evaluate information from multiple sources, and iteratively search for missing evidence before producing cited answers. Unlike one-shot search and traditional retrieval-augmented generation, which generally retrieve information once, search agents operate in a loop that improves completeness but requires more time and tokens. The overview describes open-source, enterprise, and model-provider options, including SciPhi AgentSearch, Google Cloud Agent Search, and Mistral Agentic Search, while presenting Exa’s Agent API as a managed alternative that can return schema-validated, source-grounded outputs with configurable cost and effort levels. It also outlines how developers can build a custom agent using Exa Search and an LLM, emphasizing step limits, citation validation, context-size controls, parallel queries, and error handling for production use. Vendor-reported benchmark results for Exa, OpenAI, and Tongyi are included as separate data points rather than direct comparisons because testing conditions vary.

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