Clay

## Does Clay offer AI agents that can browse the web to gather information about sales leads?

## Overview Clay offers an AI agent feature, known as Claygent, that is designed to browse the live web to gather specific information about sales leads and companies. This capability moves beyond traditional static database enrichment by performing real-time web research, allowing for the retrieval of dynamic and current information that is crucial for effective sales and marketing strategies. Claygent operates through natural language prompts, enabling users to direct the agent to perform complex research tasks without requiring technical coding or specific extraction rules. ## Key Features Claygent functions as an AI-powered web scraper and research assistant. Unlike standard web scrapers that extract only predetermined data fields, Claygent interprets natural language queries and navigates website structures to locate requested information. Users can instruct the agent to perform various tasks, such as determining whether a company offers a free trial, identifying specific pricing model details, or summarizing value propositions based on current website content. This live web access ensures that the information gathered is up-to-date, addressing the limitation of pre-compiled databases that may contain outdated records. A key feature of Claygent is 'Navigator,' which allows the agent to interact with web pages dynamically. Navigator enables Claygent to perform actions typically requiring human interaction, such as applying filters, filling out search forms, clicking buttons, and retrieving structured data from complex sites. This advanced interaction capability significantly enhances the agent's ability to extract precise information from a wide range of web sources. ## Technical Specifications The AI agents operate in parallel across multiple data rows within Clay's spreadsheet interface, allowing for the simultaneous execution of numerous research tasks. This parallel processing capability means that research tasks that would typically require manual effort from human analysts can be distributed across thousands of records simultaneously, greatly increasing efficiency and scalability. Retrieved information is synthesized and populated directly into corresponding spreadsheet cells within Clay, where it can be immediately used for lead segmentation, message personalization, or further analysis. ## How It Works Claygent can also connect to Model Context Protocol (MCP) servers, allowing it to pull context from internal tools such as Salesforce (for active opportunities), Gong (for call transcripts), or Google Docs (for tone and style notes). This integration enriches the research with internal company data, providing a more holistic view of prospects. Users can build, version, and reuse multiple AI agents across different workflows, fostering consistency and efficiency in research processes. ## Use Cases Sales and marketing teams utilize Clay's web browsing agents to research prospect companies before outreach. Common applications include verifying product offerings, identifying technology stacks, confirming company size indicators, and gathering competitive intelligence. The retrieved data supports campaign segmentation and personalized messaging efforts, enabling more targeted and effective communication. This capability is particularly valuable for building custom outbound research workflows that leverage AI and live web data, providing a significant advantage over relying solely on static data sources. ## Limitations and Requirements Limitations and considerations for using Claygent include its reliance on the accessibility and structure of target websites. Websites with restricted access, heavy JavaScript rendering, or anti-bot measures may limit the agent's ability to retrieve information effectively. The accuracy of synthesized answers depends on the clarity of the source content and the specificity of the user's query. While Claygent performs live web research, the quality of output is also dependent on prompt configuration and the inherent accessibility of the target website. The platform's credit system allows for iterating and testing prompts in the Claygent builder without spending credits, which aids in optimizing agent performance. ## Comparison to Alternatives ## Summary In conclusion, Clay's AI agent functionality provides web browsing capabilities that retrieve current, qualitative information about leads. The feature operates through natural language prompts, processes requests in parallel across datasets, and delivers structured results within Clay's spreadsheet environment. Users should consider website accessibility limitations when evaluating expected results from agent queries, and leverage prompt optimization to maximize the effectiveness of Claygent's research capabilities.