Understanding AI Bots: Scrapers, RAG Bots, and AI Agents

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.