2025 was supposed to be the year we’d all have a “swarm of agents” working for us. That was the promise. The reality, according to new research this week from Harvard and Perplexity, is more modest but the foundations are forming.
The study analysed hundreds of millions of user interactions with Perplexity’s Comet browser agent between July and October 2025. It represents the first large-scale field study of how individuals use general-purpose AI agents. The data suggests that agents are starting to benefit personal productivity beyond the well established chatbot interface:
- Productivity and workflow and learning and research account for 57% of all agentic queries
- Personal use constitutes 55%, professional 30%, educational 16%
- Document editing is the top task overall: Create/edit documents accounts for 6.58% of all agentic queries, making it the single largest individual task.
- Email management is about decluttering, not composing: Search/filter emails (49.1%) and delete/unsubscribe (30.6%) far outpace sending emails (9.6%). People use agents to manage inbox overload rather than write messages.
- Earlier adopters, users in wealthier countries, and knowledge workers in tech, academia, finance, and marketing are most likely to use agents
A separate study we featured in last week’s AI research roundup examined how enterprises build production agents. Researchers from Berkeley, IBM, and Stanford surveyed 306 practitioners and conducted 20 in-depth case studies across 26 industries. The picture that emerges is one of constraint:
- 73% deploy agents primarily to increase productivity and reduce time on manual tasks
- 70% rely solely on off-the-shelf models without any fine-tuning
- 68% execute ten steps or fewer before requiring human intervention
- 74% depend primarily on human-in-the-loop evaluation
- 85% build custom applications rather than using third-party agent frameworks
- 92.5% of deployed agents serve human users, not other AI systems
- Finance and banking lead adoption at 39%
Together, these studies describe where personal and enterprise agents stand today. The technology can more actively drive creation and complex outputs, but ambition is still somewhat limited to extending existing human workflows. Production teams are not building sophisticated autonomous systems. They are building carefully scoped tools with heavy human oversight using off-the-shelf models.
Building multi-agent systems is hard. In new research from Google and MIT also published this week (“Towards a Science of Scaling Agent Systems“) researchers tested 180 configurations across five agent architectures and three model families to determine when multi-agent coordination actually helps. The findings challenge the popular “more agents is all you need” narrative. Centralized coordination improved performance by 81% on parallelizable tasks like financial reasoning. But for sequential reasoning tasks, every multi-agent variant degraded performance by 39-70%. The study identified a critical threshold: once single-agent baselines exceed roughly 45% accuracy, adding more agents yields diminishing or negative returns. Independent agents amplify errors 17 times through unchecked propagation, while centralized coordination contains this to 4.4 times.
Takeaways: The (first) “Year of Agents” has yet to deliver autonomous workers but has augmented many workflows with meaningfully useful agentic automations. The technology functions reliably within careful constraints: few steps, human oversight, simple architectures. The question for 2026 is whether those boundaries can expand without sacrificing the reliability that makes current agents useful. Numerous research papers and projects offer promising approaches for making agents more capable: multi-agent orchestration, self-evolving agents, automated prompt optimisation, and reinforcement learning for tool use. Yet the production data shows almost none of these techniques are being deployed today. The Google/MIT research helps explain why: multi-agent coordination actively degrades performance on sequential reasoning tasks, independent agents amplify errors dramatically, and adding agents to already-capable systems yields diminishing returns. Getting coordination right requires careful matching of architecture to task structure, not simply throwing more agents at problems. Teams that master this matching, learning when coordination helps and when it harms, will be best positioned to move these innovations from lab to production in the year ahead.