Understanding AI Bots: Scrapers, RAG Bots, and AI Agents
Explore the different types of AI bots—LLM scrapers, RAG/search bots, and AI agents—and learn how each impacts detection strategies.
As AI automation accelerates, traditional detection strategies—built around static fingerprints, IP reputation, or simple bot heuristics—are becoming less reliable. Scrapers and RAG bots already require more policy-aware handling, but AI agents represent a deeper paradigm shift: they behave like humans, operate across diverse environments, and deliberately avoid detection. Below is a concise classification of the major AI bot categories:
Definition and Categories
The term “AI bot” refers to three distinct use cases, which differ significantly in purpose, architecture, and impact for detection teams:
LLM scrapers/crawlers
Collect content at scale to train large language models (LLMs). Examples include GPTBot and ClaudeBot.RAG/search bots
Retrieve real-time information to augment LLM responses, acting narrowly and only in response to specific user queries. Examples include ChatGPT-User and Claude-User.AI agents
Perform actions on behalf of users, such as navigating websites, filling out forms, or making purchases.
Detection of Scrapers and RAG Bots
LLM scrapers
Often identifiable, rate-limited, and transparent (e.g., via documented user agents). Detection decisions are frequently policy-based rather than purely technical.RAG bots
Operate on behalf of a human and typically perform targeted one-off fetches; blocking them can break legitimate functionality for AI-powered search engines and assistants.
Disruptiveness of AI Agents
AI agents are the most disruptive category because they automate high-risk workflows and challenge traditional fraud assumptions.
Types
Cloud-based agents
Run on provider infrastructure (e.g., OpenAI’s Operator).Local agents
Run on the user’s device or inside the user’s browser context (e.g., Perplexity’s Comet).
Stealth
Most AI agents deliberately avoid exposing their presence. They typically do not use custom user agent strings and avoid bot signals such as navigator.webdriver = true, blending in with legitimate browser traffic.
Detection Challenge
Automation is no longer inherently suspicious, pushing detection pipelines to shift away from static fingerprints or IP-based checks. Instead, detection must focus on intent, delegation patterns, and whether the action aligns with a real user’s goals.
Authentication Issues
Cloud-based agents often share the same IP infrastructure, making IP reputation ineffective, while local agents mimic legitimate user behavior, making them difficult to isolate or verify.