How to build an AI agent with web search

Exa’s guide explains how web search can make AI agents more capable by enabling them to retrieve current information, evaluate sources, and decide on subsequent searches rather than relying solely on model training data. It outlines the essential components of an agent—model, instructions, tools, and an execution loop—and compares no-code platforms such as Zapier Agents, low-code workflows like n8n, and full-code frameworks including LangGraph, CrewAI, and LlamaIndex. The tutorial demonstrates a Python research agent built with Anthropic’s SDK and Exa Search, using a system prompt, a JSON-schema search tool, bounded search and turn limits, and tool-result handling to generate source-cited answers. It emphasizes testing for appropriate search behavior and missing-information responses, writing explicit roles, procedures, and guardrails, and selecting models by evaluating reliability against cost. The guide also addresses common problems such as redundant searches, stale model knowledge, and expanding context windows, noting that concise search highlights can reduce token use, while comparing Exa’s search pricing with built-in search offerings from OpenAI and Anthropic.

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