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To distinguish AI-agent traffic from people and ordinary bots, combine documented user-agent identities, source verification such as published IP ranges or reverse DNS, and request or session evidence from server or CDN logs. Treat these as separate questions: who does the request claim to be, is that identity verified, and what is it doing? A browser-like user-agent does not prove a human made the request, and a bot score does not prove that an AI agent made it.
Why “human or bot?” is not enough
A request can be automated at the network level but initiated by a person. For example, someone may ask an assistant to fetch a page on their behalf. That differs from a background crawler collecting pages, even though both generate automated HTTP requests.
AI-related traffic also has different purposes. Search crawlers collect or index content, training crawlers may gather material for model training or fine-tuning, and agents can fetch pages in response to a user’s request. Cloudflare groups these behaviors as Search, Training, and Agent, and notes that one bot may perform more than one behavior. Cloudflare’s bot documentation defines a bot as “a software application programmed to do certain tasks.”
For reporting, keep purpose separate from identity and automation. A useful record can say a request is a verified named crawler, a claimed but unverified agent, likely automated with an unknown operator, or a user-directed agent fetch when the provider supports that distinction. “Likely human” should be used only when the available evidence supports it; the absence of a known bot token is not proof of a person.
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Start with server or CDN request logs
Browser analytics alone may not show all automated fetches. Google Analytics says known bot and spider traffic is automatically excluded. To measure requests made to the site, use server, CDN, or edge logs as the primary evidence, and use analytics as a separate view of browser-measured activity. See Google Analytics’ explanation of known bot-traffic exclusion and Cloudflare Bot Analytics.
Retain enough request-level context to investigate a classification instead of relying on a single label. Useful fields include:
- Request timestamp, path, and response status.
- User-agent string and source IP, or a validated network identity if the CDN supplies one.
- Provider bot fields or scores, where available.
- Session and request-rate context that helps identify patterns across multiple requests.
Identify claimed crawlers and agents
Match specific user-agent tokens against the operator’s current documentation. OpenAI, for example, documents distinct identities for different activities: OAI-SearchBot for search, GPTBot for potential training use, OAI-AdsBot for submitted ad-page checks, and ChatGPT-User for some user-initiated page visits. Consult OpenAI’s crawler overview for the documented identities and example user-agent strings.
Look for the specific documented token rather than classifying the entire string as a regular browser. Some example strings include browser-like components. A matching token is still only a claim until the request’s source is validated; user-agent strings can be copied or spoofed.
OpenAI also documents published IP ranges for its crawlers. Its documentation says OAI-SearchBot and GPTBot can be controlled independently through robots.txt, while a user-directed ChatGPT-User visit is not an automatic crawl. These distinctions help describe declared purpose and controls, but they do not authenticate an individual request by themselves. Check OpenAI’s current crawler documentation when maintaining rules or interpreting logs.
Verify a claimed identity where possible
When an operator provides a validation method, use it to distinguish a verified identity from a self-reported user-agent. Cloudflare describes two approaches for verified bots: compare the source IP against a list published by the bot owner, or perform reverse DNS checks against domains supplied by the operator. Its IP validation documentation also explains that generic user-agent patterns, such as common browsers and HTTP libraries, do not qualify as bot identities in its Verified Bots API.
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Keep the operator’s validation data current: published ranges can change. In logs and reports, preserve the method and confidence of the result—such as “verified,” “claimed,” or “inferred from behavior”—rather than flattening them into one bot label.
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If a request has no verifiable identity, or appears disguised, behavioral detection can help estimate whether it is automated. Cloudflare documents detection using heuristics and JavaScript detection, plus machine-learning analysis on Business and Enterprise plans. The signals can include request features, headers, session characteristics, and browser signals. Its bot score runs from 1 to 99 and indicates likelihood-oriented automation assessment; it is not an AI-agent identity certificate. See Cloudflare’s bot detection engines and Bot Management variables.
Use these signals as evidence for “likely automated, operator unknown” or for review—not as proof of a specific AI company, model, or purpose. Verification and automation detection answer different questions: IP or DNS checks can support identity, while scores and behavioral signals assess request or session patterns.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A practical classification workflow
- Preserve the evidence. Capture request-level server, CDN, or edge logs with timestamp, path, status, user-agent, source identity, and available bot fields.
- Match documented tokens. Compare specific user-agent tokens with current operator documentation; record the claimed identity and stated purpose.
- Validate the source. Where supported, check published IP ranges or reverse DNS, and record whether validation succeeded.
- Assess behavior separately. Review request rates, session context, headers, browser signals, and any bot score. Do not turn an automation score into an operator identity.
- Report the confidence and purpose. Distinguish verified identities, unverified claims, unknown automation, and user-directed agent activity where the evidence permits.
Choose a method that fits your site
A small site can begin with raw access logs and current crawler documentation. A site using a CDN or bot-management service can compare methods against its actual reporting needs:
| What to compare | Questions to ask |
|---|---|
| Identity strength | Does the method rely on a user-agent claim, or can it validate published IP ranges or reverse DNS? |
| Purpose visibility | Can reports distinguish search, training, and user-directed agent activity, rather than combining them as “AI bots”? |
| Detection coverage | Does it offer only signature matching, or also session, browser, heuristic, or machine-learning signals? |
| False-positive control | Can staff inspect scores and evidence before challenging or blocking traffic? |
| Data access and cost | Are request-level fields and analytics available on the product tier in use? Cloudflare documents tier-dependent bot analytics and management features in its Bot Analytics and bot documentation. |
Do not treat “verified” as “human” or “harmless”
Verification establishes an identity under a provider’s validation standard; it does not make a bot a human visitor or mean every request should be allowed. Cloudflare says its Verified Bots standard considers deterministic identification and non-abusive behavior, including respecting robots.txt and reasonable request rates. Read Cloudflare’s Verified Bots documentation, then apply your own access policy to the bot’s purpose, behavior, and your site requirements.
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