Fingerprint Adds Detection and Verification Tools for AI Agents and Assistants

Device intelligence provider FingerprintJS Inc. has expanded its platform with new tools designed to identify and verify artificial intelligence agents and assistants interacting with digital services, as reported by SiliconANGLE . As internet traffic evolves from human navigation toward autonomous AI interactions, organizations face challenges distinguishing between authorized AI tools and malicious automation such as scrapers or credential stuffing bots.

Traditional bot management tools typically rely on a binary distinction between human users and automated traffic. However, automated traffic increasingly includes legitimate customer-directed activity, such as shopping assistants comparing products or autonomous tools retrieving data. Fingerprint's expansion introduces Authorized AI Agent Detection and AI Assistant Detection to differentiate between verified automation and spoofed requests.

Authorized AI Agent Detection verifies signed AI agents operating directly within web browsers. According to the company, the system can cryptographically verify agents from providers including OpenAI, AWS AgentCore, Browserbase, Manus, and Anchor Browser. In contrast, AI Assistant Detection monitors direct HTTP traffic from systems such as ChatGPT, Gemini, and Claude. Because AI assistants often do not execute client-side JavaScript, the tool inspects originating IP addresses, reverse DNS data, provider-published network information, and declared user-agent headers to validate whether incoming requests originate from stated providers.

Fingerprint also launched its Automation Intelligence API in preview. The API classifies automated traffic without requiring client-side JavaScript, enabling deployment at the content delivery network edge, within middleware, or directly on backend servers. It pairs traffic classification with network risk context, including detection of proxies, virtual private networks, Tor connections, and geographic location signals.

Alongside detection tools, Fingerprint announced the general availability of its Model Context Protocol (MCP) Server. The integration exposes device signals, fraud telemetry, and workspace management functions to compatible development environments and assistants, including Claude Code and Cursor. The server allows fraud analysts to query incident data via natural language to investigate connected accounts or analyze transaction fraud anomalies.

The product updates come as enterprise deployment of autonomous tools accelerates. According to data from theCUBE Research's 2025 AI Builder Summit, 55% of surveyed organizations had deployed autonomous AI agents, with 60.5% planning deployments within 18 months. Multi-agent systems were deployed in 41.8% of cases, with 50.9% planning adoption. However, a companion study on Agentic AI and Trust found that only 20.2% of respondents operated enterprise-wide AI on governed frameworks, while 50.7% relied primarily on public AI tools.