How AI agents reshape knowledge work

A Perplexity and Harvard Business School study examines how its Computer agent, launched in February 2026, differs from conventional AI search by autonomously planning and executing multistep work across tools, while users focus more on defining goals and reviewing results. Based on product-usage data, matched Search and Computer sessions, task classifications, and user interviews, the study reports that Computer use grew rapidly and spans research, analysis, document creation, software, finance, operations, and other domains. In matched tasks, Computer averaged 26 minutes of machine execution compared with 33 seconds for Search, used more external connectors, and showed lower measured user dissatisfaction despite occasionally pausing for clarification or approval. The authors estimate that Computer can reduce total task time by 84–87% and estimated costs by 93–94%, though these figures depend on assumptions about human labor, oversight, and equivalent execution time. Computer users also more often attempt work outside their inferred occupations, pursue more complex and non-routine tasks, and engage a broader range of knowledge areas and work activities, particularly in software development, document production, and visualization. The study cautions that its early-adopter sample, session-matching approach, estimation methods, and focus on the Perplexity ecosystem limit generalizability, while suggesting that agents may reshape knowledge work by shifting people from manual operators to supervisors and potentially changing how roles and teams are organized.

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