Proactive Service Agents: A Unified Decision Framework, Methods, and Evaluation
A survey paper proposes an operational definition of proactive service agents centered on initiative, formulating the problem as a partially observable sequential decision process constrained by authorization and risk. It organizes existing methods along a decision pipeline: state and need estimation, intervention gating, action construction, and feedback adaptation.
The paper presents a unified decision framework for proactive service agents, where agents infer service opportunities from incomplete environmental and user signals and choose among remaining silent, asking, assisting, and acting. It accounts for interruption, misunderstanding, overreach, and privacy costs, and describes prescribed, predictive, model-based, and return-optimizing mechanisms as policy-construction components.
The framework represents timing, content, and delivery within one structured action, making explicit the option value of waiting, the decision value of questions, and feedback-induced state changes. It normalizes decision units and uses a three-axis evidence description.
The survey highlights a shift from reactive to proactive AI agents, which could enable new service models where agents initiate actions based on inferred user needs, potentially increasing user engagement but also raising concerns about interruption and privacy.
Proactive service agents could create value by anticipating user needs and reducing friction, but businesses must manage risks of overreach and user annoyance. The framework may guide product design for AI assistants and customer service agents.
Observable next signals include adoption of the proposed decision framework in agent architectures, development of benchmarks for proactive behavior, and research on balancing proactivity with user control and privacy.