What AI agent traffic is

AI agent traffic is visits driven by a specific person’s specific task, carried out by software. A shopper asks an assistant to find running shoes under $150 with next-day delivery — the agent opens several stores’ catalogues, compares product pages and, if it has permission, places the order. In July 2026 Cloudflare defined this kind of traffic as automated behaviour acting on a person’s behalf, usually in real time, to get something done right now.

Agent, crawler or scraper

Visit type Why it comes Examples robots.txt
Agent on assignment Carries out a person’s task right now ChatGPT-User, Claude-User, Perplexity-User, Google-Agent OpenAI, Perplexity and Google say their fetchers may not follow it; Anthropic says Claude-User does
AI search crawler Indexes pages for assistant answers OAI-SearchBot, Claude-SearchBot, PerplexityBot Follows it
Training crawler Collects data for models GPTBot, ClaudeBot Follows it
Scraper Collects prices and content for third parties Disguises itself as a browser Ignores it

The second and third types are covered in more detail under AI crawlers.

How to recognise an agent

  • Declared User-Agent. AI companies’ user-triggered fetchers identify themselves by name, and OpenAI and Perplexity publish lists of their IP addresses for verification.
  • Signature. Agents that sign their requests with Web Bot Auth can be verified cryptographically — the most reliable signal.
  • Behaviour. Dozens of product pages a minute, navigation with no scrolling or mouse movement, forms filled in within fractions of a second.
  • Agentic browsers. An agent working inside the user’s agentic browser may be indistinguishable from a person by both User-Agent and IP.

How it distorts metrics

Metric What happens without segmentation
Conversion rate An agent views many product pages across several stores and buys in one — the others’ CR drops
Bounce rate Short open-read-leave visits look like bounces
Unique visitors An agent in a clean browser with no cookies looks like a new visitor on every run
A/B tests Noise in the metrics and a risk of SRM
Recommendations Agent views leak into the popular and viewed-together behavioural signals

What a store should do

  • Measure the share of agents from server or CDN logs, not only in analytics: analytics filters catch known bots, but not every agent.
  • Put agents into a separate segment and a separate funnel: view, basket, order.
  • Exclude them from A/B tests and from the data used to train recommendations.
  • Do not confuse them with AI referral traffic — people who clicked a link in an assistant’s answer.
  • Check your CDN settings: from 15 September 2026 Cloudflare blocks the Agent and Training categories by default on pages with ads for newly onboarded domains.