Researchers from Perplexity and academic collaborators conducted a field study comparing a conversational AI assistant with an autonomous AI agent, finding that AI agents don't merely accelerate knowledge work — they fundamentally change its nature. The study highlights a key distinction: a conversational assistant returns information quickly (summaries, links) that a user must then act on manually, while an autonomous agent delivers completed work products such as built spreadsheets, drawn charts, and draft memos, albeit with a longer processing time.

The research, published in Harvard Business Review, suggests that AI agents broaden the scope of knowledge work by shifting what counts as a task boundary. Rather than simply helping workers find information faster, agents take on multi-step workflows end to end. This has significant implications for competitive strategy, change management, and how organizations think about productivity and the evolving role of human judgment in an AI-augmented workplace.

This research paper from Harvard Business School and Perplexity AI provides the first field evidence on how autonomous AI agents reshape knowledge work compared to conversational AI assistants. Using production data from Perplexity's Search and Computer products, the authors develop a task-based economic framework and examine four dimensions: adoption, autonomy, efficiency, and scope. The key finding is that Computer performs 26 minutes of autonomous work per session versus just 33 seconds for Search — a 48× increase — while reducing per-query dissatisfaction rates by 55%. On matched tasks, the agent reduces completion time from 269 to 36 minutes, yielding 87% time savings and 94% cost savings compared to humans using Search alone.

Beyond raw efficiency, the paper documents a fundamental expansion in the scope of work users attempt when given access to agents. Computer queries more frequently cross occupational boundaries, require higher-order cognition (71% abstract non-routine tasks vs. 53% for Search), draw on broader expertise domains (+38% more O*NET Knowledge domains per query), and bundle more subtasks into single queries. Notably, 23% of Computer queries involve task types that never appear in the same users' Search history, suggesting agents unlock entirely new categories of work rather than merely accelerating existing ones.

The conceptual framework elegantly captures the core tradeoff: agents have higher fixed delegation costs (specifying objectives, reviewing output) but lower marginal per-step execution costs, making them economically preferred for complex, multi-step tasks. User interviews corroborate the quantitative findings, with reported speedups ranging from 5× to 300× and cost reductions of two to three orders of magnitude. The paper also finds that Computer and Search are complements rather than substitutes — adopting Computer increases daily Search usage by approximately one additional query per day — consistent with the framework's prediction that agent-driven cost savings free up budget for shorter tasks where conversational tools retain an advantage.

A June 2026 paper from Perplexity researchers presents large-scale empirical evidence that autonomous AI agents fundamentally reshape knowledge work compared to conversational assistants. Using production data from Perplexity's Search (assistant) and Computer (agent) products, the study finds that agents perform 26 minutes of autonomous work per session versus just 33 seconds for assistants — a 48x increase. This autonomy translates into an 87% reduction in task completion time and a 94% reduction in cost, while simultaneously improving output quality, with per-query dissatisfaction rates 55% lower for the agent product.

Beyond speed and cost, the paper reveals that agents expand the scope of work users attempt. Computer queries more frequently cross occupational boundaries (59% vs. 50%), require higher-order cognition concentrated at the "Create" level of Bloom's taxonomy, and bundle multiple interdependent subtasks into single requests. Notably, 23% of agent queries engaged fine-grained work activities that users never attempted with the assistant, suggesting agents unlock entirely new categories of work. The authors frame these findings through a knapsack optimization model where agents' higher fixed delegation costs but lower marginal execution costs make them economically superior for complex, multi-step tasks.

The study's implications point toward a significant restructuring of knowledge work: humans shift from manual execution toward supervisory, verification, and extension roles, while agents handle planning and multi-step execution. The authors acknowledge important limitations including self-selection bias, a short observation window, and reliance on a single product ecosystem, but the magnitude of observed effects — validated through sensitivity analyses — suggests the transition from AI assistants to autonomous agents represents a structural shift in how knowledge work is organized and performed.

This research paper examines how AI agents are transforming knowledge work by analyzing production data from Perplexity's Search and Computer products. The study compares conversational AI assistants with autonomous agents that execute tasks end-to-end, revealing three key findings. First, Computer performs 26 minutes of autonomous work per session compared to just 33 seconds for Search, automating task decomposition and execution while shifting user follow-up queries toward higher-order work like verification and extension. Per-query dissatisfaction rates are 55% lower on the autonomous Computer product.

Second, the autonomy advantage dramatically reduces task completion time from 269 to 36 minutes on matched tasks, lowering estimated time and cost by 87% and 94% respectively compared to humans using Search alone. Third, and perhaps most significantly, the study finds that AI agents change the scope of work users attempt — Computer queries more frequently cross occupational boundaries, require higher-order cognition, draw on broader expertise, and bundle interdependent subtasks into composite queries. The research demonstrates that autonomous AI agents don't just speed up existing workflows but fundamentally expand the breadth and depth of automated work that users are willing to undertake.