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The Complete Guide to AI Agent Detection and Classification

AI agents now use the same login and signup pages people do, and Arkose Labs' research across billions of sessions finds that at the technical layer they all look alike. A shopping assistant acting for a real customer and a credential stuffing bot both run on cloud infrastructure, present synthetic browsers and fake device fingerprints, and click with a speed and precision no person can match. That makes asking whether traffic comes from a bot redundant. The question that matters is what an agent intends to do and whether it is authorized to do it. This position paper sorts agent traffic into three groups, good agents that identify themselves, good agents that do not, and malicious automation, and sets out the signals that separate them: consistent navigation, respect for rate limits and robots.txt, activity that lines up with real customer actions, and the impersonation mistakes attackers make. It closes with a policy spectrum, from blocking all non-human traffic to treating agents differently by endpoint, and the controls needed to move between those policies quickly.