ComBodied Agents: a New Paradigm of Human-Centric Agentic AI

2026-08-11

AI Summary

This work addresses the limitations of existing intelligent agents in effectively modeling the temporal evolution of human states and agency, which often results in support systems lacking adaptability and personalization. To overcome this, the paper introduces Combodied Agents—a novel paradigm that centers on human state trajectories and integrates multimodal perception, correctable longitudinal memory, a lightweight personal world model, and proportionate intervention strategies to form a user-controllable, uncertainty-aware closed-loop support system. The proposed framework unifies capabilities across digital and embodied agents, seamlessly incorporating software, sensors, robotics, and human-in-the-loop services. Furthermore, it advances agent evaluation through contextualized assessment protocols, agency-preserving metrics, and edge-based governance mechanisms, shifting the focus of intelligent agents from task completion toward sustained human well-being.

Abstract

After an older adult misses a medication dose, a software agent can send another reminder and an embodied agent can bring the medication. Yet neither explains whether the person forgot, is confused, has side effects, or deliberately refused, nor what support is appropriate. This reveals a structural gap in Agentic AI: Digital Agents primarily transform software states, while Embodied Agents transform physical states; neither makes a person's evolving state and agency the primary object of modeling, intervention, and evaluation. We introduce Combodied Agents, a human-centered paradigm that perceives, models, predicts, and supports individual human-state trajectories over time, using software tools, sensors, wearables, robots, and human services as action channels rather than end goals. We unify fragmented capabilities across personal assistants, health agents, AI companions, and adaptive human--AI systems into a closed loop: event-based multimodal perception reconstructs meaningful personal events; longitudinal, correctable memory provides temporal context; Personal World Models estimate future personal states and outcomes under alternative decisions and interventions; and an admissible intervention policy selects proportionate support under consent, uncertainty, safety, reversibility, and user control. Feedback from the person and environment updates the loop. Rather than requiring an exhaustive Human Digital Twin, the framework uses purpose-bounded, uncertainty-aware, user-correctable representations. We organize the design space by human-state targets, relational contexts, and agent roles, and propose scenario-centered evaluation, agency-preservation metrics, benchmark requirements, edge-native personal models, and governance directions. Combodied Agents shift Agentic AI from external task completion toward sustained human benefit.

Problem

• Agentic AI
• human state modeling
• embodied agents
• digital agents
• human-centered AI

Innovation

• Combodied Agents
• Human-Centric AI
• Personal World Models
• Longitudinal Memory
• Admissible Intervention