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

arXiv: https://arxiv.org/abs/2512.07828 · HTML: https://mlsanity.com/paper/2512.07828/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Dec 2025.

## Built on by (most cited first, 15 of 15)

- [VeRO: A Harness for Agents to Optimize Agents](https://mlsanity.com/paper/2602.22480/) (2026)
- [How are AI agents used? Evidence from 177,000 MCP tools](https://mlsanity.com/paper/2603.23802/) (2026)
- [Understanding Usage and Engagement in AI-Powered Scientific Research Tools: The Asta Interaction Dataset](https://mlsanity.com/paper/2602.23335/) (2026)
- [Distributed Denial of Science: How Indirect Data Poisoning of AI Systems Can Industrialize Scientific Fraud](https://mlsanity.com/paper/2607.10712/) (2026)
- [OmnilingualGAIA2: Evaluating the Multilingual Gap in Frontier AI Agents](https://mlsanity.com/paper/2608.08775/) (2026)
- [How AI Agents Reshape Knowledge Work: Autonomy, Efficiency, and Scope](https://mlsanity.com/paper/2606.07489/) (2026)
- [Can AI Agents Synthesize Scientific Conclusions?](https://mlsanity.com/paper/2606.11337/) (2026)
- [Distributed Legal Infrastructure for a Trustworthy Agentic Web](https://mlsanity.com/paper/2603.06884/) (2026)
- [Shopping with a Platform AI Assistant: Who Adopts, When in the Journey, and What For](https://mlsanity.com/paper/2603.24947/) (2026)
- [How Early Adopters Used Generative AI Worldwide: Variation by Country Income and Language](https://mlsanity.com/paper/2605.30685/) (2026)
- [Toward Continuous Assurance for the Democratization of AI Agent Creation in Industry](https://mlsanity.com/paper/2607.21495/) (2026)
- [Binding Biometrics with AI Agent Identifiers for Delegation of Authority](https://mlsanity.com/paper/2608.04292/) (2026)
- [Who Delegates to AI? Evidence from Agent Configurations in Github](https://mlsanity.com/paper/2608.20425/) (2026)
- [The Hard Part Comes After Search: Benchmarking Web Agents on Synthesizing, Organizing, and Displaying Knowledge](https://mlsanity.com/paper/2609.30604/) (2026)
- [PRAISE: A Reproducible Workflow for Non-Programmer Scientists to Implement Published AI Models](https://mlsanity.com/paper/2610.16980/) (2026)

## Abstract (arXiv)

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.