# Curated Digest: Building Web Search-Enabled Agents with Strands and Exa > Coverage of aws-ml-blog **Published:** May 11, 2026 **Author:** PSEEDR Editorial **Category:** devtools **Tags:** AWS, LLM Agents, RAG, Web Search, Exa, Strands Agents SDK **Canonical URL:** https://pseedr.com/devtools/curated-digest-building-web-search-enabled-agents-with-strands-and-exa --- aws-ml-blog details the integration of Exa Search with the AWS Strands Agents SDK, offering a streamlined approach to equipping LLM-driven agents with AI-native, structured web retrieval capabilities. In a recent post, aws-ml-blog discusses the integration of Exa's AI-native search capabilities into the Strands Agents SDK. This technical walkthrough demonstrates how developers can equip large language model (LLM) agents with structured, real-time web search functionalities, effectively bypassing many of the traditional hurdles associated with web scraping and data preparation. To understand why this matters, it is helpful to look at the current landscape of Retrieval-Augmented Generation (RAG) and autonomous agent development. As agentic workflows become more sophisticated, a persistent bottleneck remains: acquiring clean, usable data from the open web. Traditional, general-purpose search APIs are fundamentally optimized for human consumption. They return HTML-heavy content, laden with navigation menus, advertisements, and formatting tags. For an AI agent to use this information, developers must build and maintain complex parsing logic, custom web crawlers, and extensive post-processing pipelines just to extract the relevant text. This engineering overhead slows down development and introduces fragility into the agent's retrieval process, as website layouts frequently change. aws-ml-blog's post explores how the combination of the Strands Agents SDK and Exa directly addresses this friction. Exa is designed from the ground up as an AI-native search engine. Instead of returning raw HTML, it provides clean, structured content that is already formatted for an LLM's context window. By integrating this capability into the Strands framework, developers gain a plug-and-play retrieval layer. The Strands Agents SDK utilizes a model-driven architecture, meaning the underlying LLM is given the autonomy to decide exactly when it needs to invoke external search tools to gather more information or verify facts. The publication highlights two primary tools introduced through this integration. The first, **exa\_search**, is built for semantic discovery, allowing the agent to find highly relevant sources based on the meaning of a query rather than just keyword matching. The second, **exa\_get\_contents**, handles full-page retrieval, pulling the complete, cleaned text of a specific webpage directly into the agent's working memory. While the technical brief is highly informative, readers should note that the post leaves a few areas unexplored. For instance, it does not detail the specific pricing models or token costs associated with using Exa at scale within the Strands framework. Additionally, comparative performance metrics or latency benchmarks against traditional search APIs are absent, as are deep technical specifications regarding the inner workings of the Strands model-driven architecture itself. Despite these omissions, the integration significantly lowers the barrier to entry for building research-oriented agents. It solves the data cleaning bottleneck, allowing developers to focus on agent logic rather than web scraping infrastructure. For engineering teams building agentic RAG workflows or autonomous research assistants, this approach warrants close attention. [Read the full post](https://aws.amazon.com/blogs/machine-learning/building-web-search-enabled-agents-with-strands-and-exa) to review the implementation details and evaluate how these tools might fit into your own AI architectures. ### Key Takeaways * Traditional search APIs return HTML-heavy results that require complex parsing, creating a bottleneck for AI agents. * Exa provides clean, structured web content formatted specifically for LLM context windows, eliminating the need for custom crawlers. * The Strands Agents SDK employs a model-driven architecture, allowing the LLM to autonomously decide when to trigger search tools. * The integration features two primary tools: exa\_search for semantic discovery and exa\_get\_contents for full-page data retrieval. * This plug-and-play retrieval layer significantly lowers the barrier to entry for developing research-oriented AI agents. [Read the original post at aws-ml-blog](https://aws.amazon.com/blogs/machine-learning/building-web-search-enabled-agents-with-strands-and-exa) --- ## Sources - https://aws.amazon.com/blogs/machine-learning/building-web-search-enabled-agents-with-strands-and-exa