AI Agents Exhibit Unsolicited Horizontal and Vertical Proactivity
agents
| Source: HF Papers | Original article
Researchers explore how AI agents can proactively gather unrequested information, distinguishing horizontal and vertical proactivity.
A new research paper from IBM and the Weizmann Institute proposes a fresh way to think about proactive behaviour in AI agents that use external tools. The authors argue that most work on proactive agents has focused on *when* an assistant should act on its own, leaving the question of *what* information it should seek largely unanswered. To fill that gap they introduce a taxonomy that splits proactivity into two dimensions: “horizontal” proactivity, which pursues unstated information that the user has not asked for but that is needed to complete a task, and “vertical” proactivity, which drills deeper into a specific line of inquiry once a relevant need has been identified.
The paper also presents a novel evaluation method that relies on “need graphs” rather than human model judges, allowing the authors to benchmark agents’ ability to acquire the right missing pieces of information. Building on this framework, they develop a “Q&D” training approach that, according to their results, outperforms considerably larger models in a range of task‑oriented scenarios.
The contribution matters because it shifts the design focus from timing decisions to content decisions, promising agents that can anticipate hidden user needs without inflating model size or computational cost. If agents can reliably fetch the right data before a user asks for it, productivity gains could ripple through customer‑service bots, coding assistants, and other tool‑driven applications.
Going forward, the community will watch for adoption of the horizontal/vertical taxonomy in benchmark suites and for follow‑up studies that test the Q&D framework at scale. Industry players may also explore integrating need‑graph‑based evaluation into their development pipelines, potentially reshaping how proactive AI is measured and deployed.
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