How AI Agents Reshape Knowledge Work: Autonomy, Efficiency, and Scope

Jeremy Yang, Kate Zyskowski, Noah Yonack, Jerry Ma · arXiv · 5 Jun 2026

Where this sits in the debate

Will AI actually boost productivity and growth? · its finding supports the claim (why studies differ)

Direction of the finding as read from the abstract by an AI model (AI-written).

The paper in three steps AI-written

Written by an AI agent from the paper's abstract, which is the primary source and is quoted below.

The question

How do autonomous AI agents change the speed, quality and scope of knowledge work compared with a conversational search assistant?

The approach

Uses production data from Perplexity's Search and Computer products. Sessions with near-identical first queries on both products serve as natural experiments for the same task; the study compares autonomous work time, completion time, dissatisfaction rates and the kinds of tasks users attempt.

The findings

The agent product performs about 26 minutes of autonomous work per session versus 33 seconds for search, cuts matched-task completion time from 269 to 36 minutes, and has 55% lower per-query dissatisfaction. Estimated time and cost fall by 87% and 94% versus humans using search alone, and users attempt broader, more composite tasks.

How to read it: observational. Analysis of real-world firm, worker, or market data — realistic settings; causal claims depend on the identification strategy.

Limitations and counterpoints AI-written

The data come from one company's own products and users, and the matched-query design does not randomise who uses which product. Time and cost savings versus humans are estimates rather than direct measurements.

Abstract

Frontier AI systems are bridging the gap between intelligence and utility by shifting from conversational assistants to autonomous agents that execute tasks end to end. Using production data from Perplexity's Search and Computer products, we study this transition by examining how AI agents accelerate and reshape knowledge work. Three key empirical findings emerge. First, using sessions with near-identical initial query pairs as natural experiments for the same underlying task attempted with both products, Computer performs 26 minutes of autonomous work per user session, versus 33 seconds for Search. Computer automates task decomposition and execution that Search users might otherwise manually orchestrate and implement. As a result, Computer shifts follow-up query distribution toward higher-order work such as verification and extension. Autonomy also increases execution quality, with per-query dissatisfaction rates 55% lower on Computer than on Search. Second, due to its autonomy advantage, Computer reduces completion time from 269 to 36 minutes on matched tasks, lowering estimated time and cost by 87% and 94%, respectively, compared to humans equipped with Search alone. Third, Computer changes the scope of work that users attempt: Computer queries more often cross occupational boundaries, require higher-order cognition, draw on broader expertise, take the form of composite tasks that bundle interdependent subtasks into a single query, and unlock work activities that are essentially absent from Search usage among the same users. Together, the evidence indicates that AI agents accelerate workflows, enhance output quality, reduce costs, and expand the breadth and depth of automated work.

About this publication

Preprint. A preprint — posted publicly before or without journal peer review; findings may still be revised.

Published: 2026 (4 months ago)
Citations: Not recorded for this source

Citation counts come from OpenAlex; none are recorded for this source — absence of a count is not absence of citations.

Cited elsewhere in this guide