AgentQL

AgentQL from TinyFish is specialized infrastructure for enabling AI agents to interact with websites at scale, featuring a runtime environment called Mino, a purpose-built query language for web interaction, and a reasoning model optimized for web operations achieving 95% accuracy. While traditional web scraping breaks when sites change their HTML structure and browser automation requires brittle element selectors, AgentQL provides a semantic layer where agents describe what they want to accomplish rather than specifying exact technical steps. The platform's Brain component learns from discovered patterns and adapts to website variations without explicit instructions for each scenario, while the Eyes and Hands mechanisms handle perception and interaction with web interfaces. AgentQL solves critical challenges preventing reliable autonomous web interaction, including authentication barriers, dynamic content, pop-ups, form complexity, and the need to scale single workflows across thousands of different websites simultaneously. TinyFish positions this infrastructure as foundational for the next trillion users, recognizing that much of the world's information and functionality remains locked behind web interfaces that AI agents struggle to navigate reliably. The platform serves companies like Google, Amplemarket, Jobright, and DoorDash across use cases from hotel search aggregation to AI-powered job matching and data science operations.

How It Works

AgentQL operates through three integrated layers that handle reasoning, perception, and action for autonomous web interaction. The Brain layer receives high-level objectives from agents like search for hotels meeting specific criteria or extract product information from multiple retailers, then formulates strategies for accomplishing these goals across potentially unfamiliar websites. Rather than following scripted element selection rules that break when sites change, the Brain learns from patterns it discovers during operation and generalizes approaches across similar web interfaces. The Eyes layer handles perception, analyzing page structures to understand layouts, identify interactive elements, recognize content sections, and map the page's semantic structure even when HTML implementation varies significantly from other sites. The Hands layer executes actions such as clicking buttons, filling forms, navigating menus, handling authentication, dismissing pop-ups, and scrolling to load dynamic content, adapting interaction techniques based on how specific sites implement these elements. The AgentQL query language enables agents to specify what they want to accomplish using semantic descriptions rather than CSS selectors or XPath expressions, letting the system determine how to achieve goals rather than requiring explicit technical instructions. This architecture achieves 95% success rates by learning from experience and adapting to variations rather than depending on fragile hard-coded rules. For scaling, workflows defined for one website automatically adapt when deployed across thousands of different sites, with the system handling site-specific variations without manual configuration.

Core Features

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  Mino Runtime Environment provides a specialized execution context designed specifically for web agent operations. Unlike general-purpose automation frameworks, Mino handles the unique challenges of autonomous web interaction including asynchronous page loading, JavaScript-heavy interfaces, authentication flows, CAPTCHA handling, rate limiting, and session management. This purpose-built environment enables reliable agent operation across diverse websites without agents needing to implement site-specific edge case handling.

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  AgentQL Query Language enables semantic specification of web interactions rather than technical element selection. Instead of writing brittle CSS selectors like button.submit.primary that break when developers change class names, agents describe intentions like click the checkout button or extract product prices, letting AgentQL determine how to accomplish these goals on specific pages. This semantic approach creates maintainable automation that survives website redesigns.

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  Navigate Capabilities allow agents to access any website and page including content behind authentication barriers, pop-ups, cookie consents, and other obstacles users typically encounter. The system handles login flows, remembers credentials securely, dismisses interruptions automatically, navigates multi-step wizards, and adapts to unexpected page structures. This comprehensive navigation capability unlocks access to information and functionality that traditional scraping cannot reach reliably.

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  Execute Complex Workflows supports sophisticated multi-step operations from inception to completion including form submission, checkout processes, data collection across multiple pages, file downloads, and interactions spanning multiple related sites. Rather than limiting agents to simple data extraction, AgentQL enables transactional operations where agents complete end-to-end processes autonomously with error handling and retry logic for reliability.

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  Scale Across Thousands of Sites deploys a single workflow definition across massive numbers of different websites simultaneously. A hotel search workflow created for one travel site automatically adapts when run against hundreds of other travel sites, with AgentQL handling variations in page structure, interaction patterns, and data formats. This scaling capability makes previously impractical aggregation and comparison tasks economically viable.

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  95% Success Rate demonstrates production-grade reliability through a reasoning model optimized specifically for web operations. Unlike general-purpose language models that struggle with website interaction complexity, AgentQL's Brain learns from discovered patterns during operation and applies this knowledge to new scenarios, achieving consistency that makes autonomous web agents practical for business-critical applications rather than experimental projects.

Who This Is For

AgentQL serves companies building AI agents that need reliable web interaction capabilities to access information or complete tasks on websites at scale. Data science teams aggregating information from multiple sources benefit from AgentQL's ability to extract structured data from thousands of different websites using single workflow definitions. AI application developers building assistants that help users with web-based tasks like research, comparison shopping, or form filling leverage AgentQL's navigation and execution capabilities to create reliable user experiences. Companies with data collection operations currently using manual processes or brittle scraping scripts can transition to AI agents that adapt to website changes rather than requiring constant maintenance. Enterprises integrating web-based workflows into internal systems use AgentQL to automate multi-step processes like vendor research, competitive intelligence, or supply chain monitoring across numerous external sites. The platform particularly suits organizations where web interaction reliability directly impacts business outcomes, such as travel metasearch requiring accurate real-time pricing, recruiting platforms matching candidates with job postings, or e-commerce tools tracking competitor offerings. Companies with modest web automation needs or access to structured APIs rather than web interfaces may find AgentQL's sophisticated infrastructure unnecessary, but organizations facing the scale and reliability challenges of autonomous web interaction at production scale will find the platform's purpose-built approach valuable for overcoming limitations of general-purpose automation tools.