How AI Agents Are Transforming Complex Enterprise Tasks with Perplexity

New adoption data from Perplexity reveals how AI agents are driving significant workflow efficiency gains by taking over complex enterprise tasks — offering one of the most comprehensive field studies of general-purpose AI agents to date.

For the past year, the technology sector has operated under the assumption that the next evolution of generative AI would advance beyond conversation into action. While Large Language Models (LLMs) serve as a reasoning engine, "agents" act as the hands — capable of executing complex, multi-step workflows with minimal human supervision.

Until now, however, visibility into how these tools are actually being utilised in real-world environments has remained opaque, relying largely on speculative frameworks or limited surveys.

📊 What Perplexity's Data Actually Shows

New data released by Perplexity — analysing hundreds of millions of interactions with its Comet browser and assistant — provides the first large-scale field study of general-purpose AI agents. The findings indicate that agentic AI is already being deployed by high-value knowledge workers to streamline productivity and research tasks.

"Understanding who is using these tools is essential for forecasting internal demand and identifying potential shadow IT vectors."

The study reveals marked heterogeneity in adoption. Users in nations with higher GDP per capita and educational attainment are far more likely to engage with agentic tools. More telling for corporate planning is the occupational breakdown.

These early adopters are not dabbling. The data shows that "power users" — those with earlier access — make nine times as many agentic queries as average users. This indicates that once integrated into a workflow, the technology becomes indispensable.

🤖 AI Agents: Partners for Enterprise Tasks, Not Butlers

A common view suggests agents will primarily function as "digital concierges" for rote administrative chores. However, the data directly challenges this assumption: 57 percent of all agent activity focuses on cognitive work.

Perplexity's researchers developed a "hierarchical agentic taxonomy" to classify user intent, revealing that the usage of AI agents is practical rather than experimental. The dominant use case is:

Real-world examples from the study illustrate how this translates to enterprise value:

• A procurement professional used the assistant to scan customer case studies and identify relevant vendor use cases before negotiations.
• A finance worker delegated the tasks of filtering stock options and analysing investment information autonomously.

The study defines agents as systems that "cycle automatically between three iterative phases to achieve the end goal: thinking, acting, and observing." This capability allows them to support deep cognitive work — acting as a thinking partner rather than a simple butler.

🔄 Stickiness and the Cognitive Migration

A key insight for IT leaders is the "stickiness" of AI agents once embedded into enterprise workflows. The data shows that users exhibit strong within-topic persistence in the short term — if a user engages an agent for a productivity task, their subsequent queries are highly likely to remain in that domain.

However, the user journey often evolves over time. New users frequently "test the waters" with low-stakes queries — such as movie recommendations or general trivia. The study notes that query shares tend to migrate toward cognitively oriented domains like productivity, learning, and career development.

Once a user employs an agent to debug code or summarise a financial report, they rarely revert to lower-value tasks. The 'Productivity' and 'Workflow' categories demonstrate the highest retention rates of all categories tracked.

🌐 Where AI Agents Operate: The Enterprise Stack

The "where" of agentic AI is just as important as the "what." Perplexity's study tracked the specific websites and platforms where these agents operate. The top environments are staples of the modern enterprise stack:

• Google Docs — primary environment for document and spreadsheet editing
• LinkedIn — dominates professional networking tasks (top 5 environments = 96% of queries)
• Coursera & Research Repositories — split environment for learning and academic research
• GitHub — code review, debugging, and software development workflows

🔐 CISO & Compliance Alert

AI agents are not just reading data — they are actively manipulating it within core enterprise applications via browser control and external API actions. When an employee tasks an agent to "summarise customer case studies," the agent is interacting directly with proprietary data. The perimeter for data loss prevention must be redefined accordingly.

📋 Business Planning for Agentic AI: Three Immediate Actions

The data from Perplexity confirms that we have passed the speculative phase. Agents are currently being used to plan and execute multi-step actions, modifying their environments rather than just exchanging information. Operational leaders should consider three immediate actions:

Source: Perplexity AI — Agentic Adoption Research, 2025.