Building web search-enabled agents with Strands and Exa

2026-05-11 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Robotics & Autonomous Systems · Depth: Intermediate, long

Summary

The Strands Agents SDK, an open-source framework from AWS, now integrates with Exa, an AI-native web search engine, to enhance AI agents with real-time, structured web information. This integration addresses the limitations of general-purpose search APIs by providing clean, LLM-consumable content without requiring extensive post-processing. The Strands Agents SDK uses a model-driven architecture where the LLM autonomously decides when and how to use tools. The Exa integration exposes two core tools: `exa_search` for semantic web search with category and filter support (e.g., news, research papers, GitHub) and `exa_get_contents` for retrieving full-page content from specified URLs, including live crawling for freshness. This enables agents to perform multi-step tasks like deep research, fact-checking, and competitive intelligence by incorporating current web knowledge directly into their reasoning loops.

Key takeaway

For AI Engineers building web search-enabled agents for research or competitive intelligence, integrating Strands Agents SDK with Exa simplifies access to structured, real-time web data. You should leverage the `exa_search` and `exa_get_contents` tools to enable your agents to autonomously perform multi-step information gathering, reducing hallucination and improving token efficiency by working with distilled, relevant content.

Key insights

Integrating AI-native search engines with agent SDKs provides structured, real-time web data for LLM-driven workflows.

Principles

• Model-driven agents autonomously select and use tools.
• Semantic search improves relevance over keyword matching.
• Structured content reduces LLM post-processing overhead.

Method

The Strands Agents SDK uses an agent loop where an LLM, guided by a system prompt, calls `exa_search` and `exa_get_contents` tools to iteratively gather and synthesize web information for multi-step tasks.

In practice

• Use `exa_search` with category filters for targeted content.
• Control token usage by setting `maxCharacters` on content.
• Employ `deep` search mode for comprehensive research tasks.

Topics

• Strands Agents SDK
• Exa Search Engine
• AI Agent Development
• Web Search Tools
• Semantic Search

Code references

• strands-agents/samples
• strands-agents/tools
• strands-agents/sdk-python

Best for: AI Engineer, Machine Learning Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.