The Adoption and Usage of AI Agents: Early Evidence from Perplexity

2025-12-08

AI Summary

This study investigates the adoption patterns, usage intensity, and task distribution of general-purpose AI agents in open web environments. Leveraging hundreds of millions of anonymized interaction logs from Perplexity’s Comet browser, we propose the first hierarchical agent task taxonomy—systematically categorizing 90 distinct tasks and uncovering multi-level thematic patterns. Through large-scale behavioral analysis, hierarchical clustering, and scenario-based modeling, we identify a pronounced shift from basic personal tasks (55% of queries) toward cognitively intensive activities: productivity and learning/research queries now constitute 57%. Early adopters are significantly concentrated among high-income, highly educated users and knowledge-intensive industries. To our knowledge, this is the first work to empirically characterize the macro-level usage landscape of AI agents using ultra-large-scale real-world data—establishing an empirical benchmark and theoretical framework for agent design, human-agent collaboration, and technology diffusion research.

Technology Category

• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Humans and AI: User Experience and Usability
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Search and Retrieval-Augmented AI: Agentic search

Abstract

This paper presents the first large-scale field study of the adoption, usage intensity, and use cases of general-purpose AI agents operating in open-world web environments. Our analysis centers on Comet, an AI-powered browser developed by Perplexity, and its integrated agent, Comet Assistant. Drawing on hundreds of millions of anonymized user interactions, we address three fundamental questions: Who is using AI agents? How intensively are they using them? And what are they using them for? Our findings reveal substantial heterogeneity in adoption and usage across user segments. Earlier adopters, users in countries with higher GDP per capita and educational attainment, and individuals working in digital or knowledge-intensive sectors -- such as digital technology, academia, finance, marketing, and entrepreneurship -- are more likely to adopt or actively use the agent. To systematically characterize the substance of agent usage, we introduce a hierarchical agentic taxonomy that organizes use cases across three levels: topic, subtopic, and task. The two largest topics, Productivity & Workflow and Learning & Research, account for 57% of all agentic queries, while the two largest subtopics, Courses and Shopping for Goods, make up 22%. The top 10 out of 90 tasks represent 55% of queries. Personal use constitutes 55% of queries, while professional and educational contexts comprise 30% and 16%, respectively. In the short term, use cases exhibit strong stickiness, but over time users tend to shift toward more cognitively oriented topics. The diffusion of increasingly capable AI agents carries important implications for researchers, businesses, policymakers, and educators, inviting new lines of inquiry into this rapidly emerging class of AI capabilities.

Problem

• Analyzes adoption patterns of AI agents across user demographics
• Characterizes usage intensity and primary application domains of AI agents
• Examines evolution of user tasks from practical to cognitive focus

Innovation

• Large-scale field study of AI agent adoption
• Hierarchical taxonomy for classifying agent use cases
• Analysis of user behavior shifts over time