Agentic AI has recently been moving through a period of heightened excitement and innovation, but empirical data on how these tools are actually being used has been scarce. The new study “The Adoption and Usage of AI Agents: Early Evidence from Perplexity,” by Jeremy Yang, Assistant Professor of Business Administration at Harvard Business School and affiliate with the Digital Data Design Institute at Harvard (D^3), and a team of researchers at Perplexity offers a comprehensive look at agentic AI usage in the wild. Analyzing hundreds of millions of anonymized interactions with Comet, Perplexity’s AI-powered browser, and Comet Assistant, its embedded AI agent, the findings reveal not just who the early adopters are, but the specific tasks they’re delegating and how usage evolves over time.
Key Insight: Not Your Average Chatbot
“We define agentic AI systems as AI assistants capable of autonomously pursuing user-defined goals by planning and taking multi-step actions on a user’s behalf to interact with and effect outcomes across real-world environments.” [1]
Rather than simply exchanging text in a conversation as a chatbot would, agentic AI can plan, decide, and act across multiple steps at the user’s request. In the context of the Comet browser, this means the Comet assistant can navigate websites, click buttons, fill fields, and iterate towards a goal instead of simply responding with text. For example, when you ask an agent to “unsubscribe me from all promotional emails I receive more than twice per month,” [2] it doesn’t just tell you how, it actually searches your inbox, identifies the offending senders, and unsubscribes on your behalf. Given this emphasis on modifying external environments, they don’t classify all tool use as agentic, which helps focus attention on these new AI systems and capabilities as they move into use at work and in everyday life.
Key Insight: Agents Are Mostly Used for Utility and Knowledge Work
“The two largest topics—productivity and learning—together account for 57% of all queries.” [3]
When the researchers introduced a hierarchical taxonomy spanning topics, subtopics, and tasks, clear patterns emerged about what people actually delegate to agents. Productivity and Workflow dominates at 36% of queries, with document editing, account management, and email management as the largest subtopics. Users also tend to stay within the same categories once they start delegating tasks in the short term, showing strong ‘stickiness’ across personal, professional, and educational settings. When they do branch out, they are far more likely to shift toward productivity, learning, or media tasks. Over the longer term, a bigger query share gravitates toward productivity and learning-related tasks. As users repeatedly invoke agents for these categories of tasks, it suggests that agents do become part of cognitive workflows rather than one-off, simple tasks.
Key Insight: A Personal Assistant for Personal Pain Points
“We also document heterogeneity in use cases across occupation clusters, reflecting the degree to which they align with each occupation’s task composition.” [4]
Users deploy the agent to solve the specific friction points of their industry. Finance professionals are heavily focused on efficiency, dedicating 47% of queries to productivity tasks. Students are focused on utility, with 43% of tasks allocated to learning and research. In design and hospitality, it’s even easier to see how context-specific usage dominates, from media work for designers to travel planning for hospitality staff. Ultimately, the data shows that the agent is highly versatile and reflects the specific needs of its user. In an educational context, it is a specialized research engine while in a professional context, it becomes a multi-purpose assistant. Personal contexts account for over half of all query volume. The environments where agents operate reinforce this pattern: usage clusters tightly around a small set of platforms like Google Docs, email platforms, and LinkedIn.
Why This Matters
For business leaders and executives, this study serves as a critical signal amidst the noise of AI speculation. The data confirms that we are moving from an era of generative AI to agentic AI, and AI-powered browsers may provide the onramp. Operationally, start where tasks are frequent, where environments are concentrated, and where risk can be bounded through supervision. The shift in user behavior over time indicates that once employees hurdle the initial learning curve, these tools can become sticky, essential components of the digital workflow.
Bonus
To understand more about how agents fit into the evolution of AI from tool to teammate, check out When Software Becomes Staff.
References
[1] Jeremy Yang et al., “The Adoption and Usage of AI Agents: Early Evidence from Perplexity,” arXiv preprint arXiv:2512.07828 (2025): 2. https://doi.org/10.48550/arXiv.2512.07828
[2] Yang et al., “The Adoption and Usage of AI Agents,” 6.
[3] Yang et al., “The Adoption and Usage of AI Agents,” 17.
[4] Yang et al., “The Adoption and Usage of AI Agents,” 22.
Meet the Authors
Jeremy Yang is an Assistant Professor of Business Administration at Harvard Business School and affiliated with the Digital Data Design Institute at Harvard (D^3).
Noah Yonack is a Data Scientist at Perplexity.
Kate Zyskowski is an UX Researcher at Perplexity.
Denis Yarats is Co-Founder and CTO of Perplexity.
Johnny Ho is Co-Founder and Chief Strategy Officer at Perplexity.
Jerry Ma is VP Global Affairs & Deputy CTO of Perplexity.