We present a framework for embedding abstract motivational principles into concrete AGI systems, bridging the gap between the formal theory of motivational structures and dynamics and the practical implementation of motivational systems for real-world applications and agents. We introduce MetaMo, a category-theory-based framework designed to ensure dynamical stability, self-coherence, and ethical alignment in open-ended AGI systems. MetaMo integrates a comonadic appraisal process with a decision monad, forming a pseudo-bi-monad structure that guides multi-objective reasoning and context-sensitive modulation of goals. The framework ensures that agents can pursue multiple, potentially conflicting goals while maintaining stability through contractive updates and over goals that enforce ethical constraints. We demonstrate the specialization of MetaMo in the Hyperon AGI system, where the agent’s goals are organized into a hierarchical structure in line with the MAGUS motivational theory, with top-level meta-goals focused on the two principle drives of Open-Ended Intelligence theory, individuation (self-preservation) and transcendence (self-expansion). These high-level goals dynamically influence the agent’s decision-making and appraisal processes via the OpenPsi motivational dynamic, modulating exploratory behaviors and caution based on context. OpenPsi provides a flexible and context-sensitive appraisal system that updates emotional and motivational states in response to stimuli, supporting adaptive behavior in complex environments. By combining theoretical foundations (MetaMo) with more concretely grounded motivational frameworks (OpenPsi, MAGUS), we provide a concrete approach to integrating motivational systems into AGI architectures, ensuring ethical behavior, stability, and the ability to adapt and open-endedly self-modify over time.