Designing frame-embedded reward mechanisms for APM agents

Design reward mechanisms embedded within the normative frame of agents in Agentic Business Process Management (APM) systems that make adherence to the frame beneficial for the agent while preserving agent autonomy.

Background

Within Agentic Business Process Management, framed autonomy requires that autonomous agents act in alignment with a normative frame that encodes organizational norms, goals, constraints, and process logic. A central difficulty is not only exposing such a frame to agents but ensuring that they internalize it and treat frame compliance as instrumentally valuable.

This challenge motivates the need for incentive and reinforcement mechanisms that define, measure, and strengthen an agent’s frame compliance as a continuous, learnable property. Unlike humans, AI agents cannot be motivated through traditional compensation or recognition; instead, their internal utility models must be shaped so that frame adherence is beneficial without sacrificing autonomy.

The open problem calls for the design of reward structures that are inherently part of the normative frame so that agents learn to value and prioritize frame-compliant behaviors while maintaining the flexibility and proactivity that characterize autonomous agents.