Automated software retrieving web resources specifically for AI model training or real-time AI answer retrieval.
An AI crawler retrieves web resources for an AI-related purpose, such as gathering material that may be used for training or discovering sources for search answers. The label covers different activities and does not identify one universal bot. A provider may document separate agents for automatic crawling and user-triggered retrieval.
The purpose matters when deciding access. Allowing a search-discovery crawler is not necessarily the same as permitting a training crawler, and blocking one name does not automatically control every service. Provider documentation should define which identifier and mechanism apply to the decision you want to make.
A log entry also needs careful interpretation. A claimed user-agent is not, by itself, proof of identity, and a request does not establish that the page was used in training, cited in an answer or recommended to a customer. Keep observed requests separate from conclusions about downstream use.
Takeaway: Identify the provider and crawler purpose before changing access rules. Treat crawl logs as evidence of requests, not proof of model training or commercial visibility.
Example
A manufacturer wants its public specification pages discoverable in a search experience while making a different choice about model-training collection. Its developer checks the provider’s documented agents and controls instead of applying a broad rule based only on the letters AI in a bot name.
What to check
Review both crawler instructions and infrastructure restrictions, such as a firewall that may block legitimate requests. Verify identity using supported provider information where needed. Use real access controls for confidential material; a voluntary crawler instruction is not a substitute for authentication.
For more detail, see OpenAI crawler documentation.