Our findings show that professionals use the Comet Assistant to solve specific friction points in their industry. Finance professionals prioritize efficiency with 47% of queries on productivity, while students focus on utility, dedicating 43% to learning and research.
@perplexity_ai
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Agent Adoption Stickiness: Marketing Sales Management Outpace Digital Tech
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Digital technology leads in volume (30% of queries), but fields like Marketing, Sales, and Management show more "stickiness." Once these users adopt an agent, their usage intensity outpaces their adoption numbers as they integrate the assistant into their daily workflow.
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Agent Activity Dominated by Productivity and Learning Work
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More than half of all agent activity focuses on cognitive work. Agent use is dominated by Productivity & Workflow (36% of queries) and Learning & Research (21%). Users rely on the assistant to think through something, synthesize findings, and take action on those learnings.
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Agent Query Patterns: Personal, Professional, and Educational Use Cases
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Most agent queries come from personal use (55%), followed by professional (30%) and educational (16%) contexts. New users often start with low-stakes questions like travel or trivia. Over time, they shift to more complex topics like productivity, learning, and career advice.
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Harvard Perplexity Study Reveals Large-Scale AI Agent User Behavior
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Harvard and Perplexity researchers conducted the first large‑scale field study of how people use AI agents. We analyzed hundreds of millions of anonymized user interactions in Comet to answer three questions: who’s using agents, how much, and what for.
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BrowseSafe Open-Source Tool Hardens Autonomous Agents Against Prompt Injection
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BrowseSafe and BrowseSafe-Bench are fully open-source. Any developer building autonomous agents can immediately harden their systems against prompt injection. Read more:
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BrowseSafe Fine-Tuned Model Outperforms Safety Classifiers
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Our findings show that our fine-tuned BrowseSafe model outperforms both off‑the‑shelf safety classifiers and frontier LLMs used as detectors. These gains are possible through fine-tuning on BrowseSafe-Bench data, allowing us to bypass the reasoning latency of larger models.
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BrowseSafe: Detection Model Against Prompt Injection Attacks
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BrowseSafe is a specialized detection model to defend against evolving prompt injection attacks. It is designed specifically to spot and block malicious instructions hidden in web pages before they can impact AI browser agents.
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BrowseSafe-Bench: Security Benchmark for AI Browser Agents
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BrowseSafe-Bench is our security benchmark designed to evaluate the robustness of AI browser agents against prompt injection attacks embedded in realistic HTML environments.
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BrowseSafe: Open-Source Model Prevents Malicious Prompt Injections
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Today we're releasing BrowseSafe and BrowseSafe-Bench: an open-source detection model and benchmark to catch and prevent malicious prompt-injection instructions in real-time.