Building web search-enabled agents with Strands and Exa

This article explains how to build web search-enabled AI agents using Strands Agents SDK and Exa integration for real-time, structured web information retrieval.

• Exa provides AI-native search returning clean, structured content optimized for LLM consumption
• Two core tools: exa_search for semantic web search with category filtering, exa_get_contents for full-page extraction
• Strands Agents SDK uses model-driven architecture where LLM decides tool invocation and sequencing
• Four search modes available: instant (~200ms), fast (~450ms), auto (~1s recommended), deep (~3-6s)
• Deep research assistant example demonstrates six-step workflow across news, papers, repositories, and full content
• Amazon Bedrock AgentCore Observability provides tracing and debugging for multi-step agent workflows
• Best practices: start with auto mode, control content size via maxCharacters, use category filters for precision

The integration enables agents to conduct autonomous, multi-step research across diverse sources with grounded, traceable results without hallucination.