Physical, Agentic & Nested AGI

Updated 12 July 2025

PAN AGI systems are architectures that combine physical embodiment, adaptive goal-driven behavior, and nested hierarchical organization to achieve robust general intelligence.
They integrate advancements in robotics, reinforcement learning, and multi-level reasoning to address shortcomings of monolithic AI designs.
This approach enables continuous learning, scalable decision-making, and enhanced adaptability in dynamic, multi-modal environments.

A Physical, Agentic, and Nested (PAN) AGI system is a class of artificial general intelligence architectures that are defined by (P) grounding in physical embodiment or simulation, (A) adaptive, goal-driven agentic behavior, and (N) hierarchical or nested internal organization. The PAN paradigm addresses limitations in classical symbolic, purely neural, or monolithic AI designs by emphasizing real-world interaction, autonomy, and deep compositional structure. Research and system proposals in this field integrate developments from cognitive robotics, knowledge representation, agent modeling, and modern multi-modal learning frameworks.

1. Defining Characteristics of PAN AGI Systems

The PAN framework is built upon three interlinked principles:

Physical: The system is embodied either in a real-world agent (robot) or a simulation that provides rich, multimodal streams of sensory data, and enables direct action. This embodiment allows grounding of perception, action, and cognition in the physical or simulated environment, as exemplified by the AGINAO system’s implementation on the NAO humanoid robot, where atomic sensory concepts map directly to robot sensors and actuator concepts mediate physical movement (Skaba, 2018).
Agentic: The system functions as an autonomous agent, making decisions, acting to achieve goals, and adapting via interaction and feedback. Agentic behavior encompasses self-initiated exploration, planning under uncertainty, and the maintenance of a goal-directed sense of self. Formal models—including reinforcement learning, belief-desire-intention (BDI) logic, and deontic normative reasoning—are integrated to support autonomy and self-regulation (Srinivasa et al., 2021).
Nested: Internal representations, knowledge, and behavioral policies are organized hierarchically or recursively. PAN AGI systems use multi-level abstractions, where low-level modules process raw sensory data and higher levels integrate these outputs, reflecting spatial, temporal, and conceptual dependencies in the world. This nested hierarchy facilitates open-ended, cumulative learning and supports complex compositional reasoning (Skaba, 2018, Latapie et al., 2020).

These characteristics are observed in existing and proposed AGI systems with diverse implementations, from simulated universal Turing machines with self-generating subroutines, to modular architectures with dynamic agent composition and multi-modal fabric interconnects (Dollinger et al., 24 Nov 2024).

2. Cognitive Architectures and Knowledge Representation

PAN AGI systems employ architectural components that support physical grounding, agentic reasoning, and nested structure:

Self-Programming Cognitive Layer: AGINAO’s cognitive engine evolves through self-generated “codelets” (short program fragments) assembled probabilistically and integrated through heuristic search and reinforcement learning on a virtual UTM. Each codelet/subroutine represents a concept and is evaluated using binary space partitioning and intrinsic rewards based on self-information (Skaba, 2018).
- Exploration–exploitation in PAN AGI systems is often formalized:
$P(\text{Exploration}) = \frac{Q_{\text{const}}}{Q_{\text{const}} + \sum_i Q_i} \,, \qquad P(a_i) = \frac{Q_i}{\sum_j Q_j}$

with temporal-difference updates for value estimation:

$Q_t \leftarrow Q_t + \alpha [r + \gamma V_{t+1} - Q_t]$
Universal Knowledge Models: Some architectures integrate unformalized (natural language, images), partially formalized (databases, mathematical models), and fully formalized (ontologies, algorithms) knowledge representations using meta-graph-based structures such as “archigraphs” (Sukhobokov et al., 11 Jan 2024). This supports flexible, multi-modal knowledge storage and reasoning, with modules for consciousness, subconscious processes, reflection, emotion, and ethical assessment.
Hierarchical Reasoning Engines: The Deep Fusion Reasoning Engine (DFRE) framework demonstrates how knowledge graphs structured into abstraction layers (L0: sensory, L1: symbolic, L2: high-level rules, L*: meta-goals) support both distributed reasoning and manage combinatorial complexity through Focus of Attention (FoA) (Latapie et al., 2020).

3. Agency and Systems Theory

The agentic dimension in PAN AGI is grounded in both computational and physical theory:

Physical Foundation of Agency: Agency is modeled as a system's capacity to transform low entropy (structured energy) into new information, rooted in irreversible thermodynamic processes. This provides a basis for modeling agentic behavior as a physical process, with information generation quantified by:

$\Delta S > 0, \qquad I = \log 2$

where the rise in entropy is associated with choice and action (Rovelli, 2020).

Emergent Behaviors: Systems-theoretic approaches emphasize that capabilities (such as causal reasoning and metacognition) can emerge from feedback loops, agent–environment interaction, and agent–agent communication—even if base agents are relatively simple. The dynamic act–sense–adapt cycle underpins adaptive, robust agentic intelligence (Miehling et al., 28 Feb 2025).
Multi-Agent and Nested Organization: Distributed architectures allow nesting of agents at multiple levels (from sensorimotor beads to high-level strategic planners), supporting collective agency and emergent intelligence (Srinivasa et al., 2021).

4. PAN Architectures in Practice: Embodiment, Learning, and Adaptation

Physical Embodiment: Embodied AGI systems are classified along a five-level taxonomy (Wang et al., 20 May 2025):
- L1–L2: single/composite task completion with limited adaptation.
- L3–L4: spanning conditional general-purpose task handling, multi-modal perception, and adaptable planning.
- L5: full general-purpose, human-like cognitive and sensorimotor functions, with self-awareness and memory reconsolidation.

Modern frameworks—such as Agentic Robot—emphasize structured planning, closed-loop verification, and brain-inspired modularity to support reliable sequential manipulation (Yang et al., 29 May 2025).

Learning Approaches: PAN AGI systems integrate unsupervised, self-programming, and reinforcement learning mechanisms. Hierarchical architectures incorporate lifelong learning, meta-learning, and continual adjustment of internal representations, enabling models to support robust generalization and transfer (Cichocki et al., 2020).
Self-Organization and Meta-Learning: Architectures incorporate goal management, self-monitoring, reflection, and meta-learning blocks, allowing systems to reorganize internal strategies in light of performance and environmental feedback (Sukhobokov et al., 11 Jan 2024).

5. Vision, Perception, and World Modeling

Generative–Discriminative Integration: PAN AGI vision systems employ both discriminative models for efficient, bottom-up perception, and generative models for reconstructing complete latent representations. Unified integration is sought for scalability and real-world generalization, but challenges remain in balancing computational cost, flexibility, and invariance learning (Potapov et al., 2018).
World Models: The primary objective of a world model in a PAN AGI system is to simulate all actionable possibilities of the real world for decision-making. New architectures propose hierarchical, multi-level, and mixed discrete/continuous representations:

$\hat{s}' \sim p_f(\hat{s}' | \hat{s}, a), \quad o' = g(\hat{s}')$

and recommend generative self-supervised learning to ground predictions and avoid collapse in latent-only objectives (Xing et al., 7 Jul 2025).

Hierarchical Abstraction: World models and knowledge graphs are structured to enable reasoning at both fine-grained sensory and high-level symbolic abstraction, ensuring both physical fidelity and scalability for complex, open-ended planning tasks (Xing et al., 7 Jul 2025, Latapie et al., 2020).

6. Practical System Designs, Evaluation, and Applications

System-Level Integration: PAN AGI frameworks, such as the Open General Intelligence (OGI) architecture, use modular, specialized processing areas, fabric interconnects for dynamic routing, and dynamic weighting functions for adaptive behavior:

$w_t = \Phi(C, E_t) = \text{softmax}(g(C, E_t))$

providing real-time adaptability, multi-modal integration, and scalable deployment (Dollinger et al., 24 Nov 2024).

Agentic System Evaluation: Advanced agentic frameworks assess performance via graph-structured decomposition, tool integration metrics, and structural similarity indices (e.g., Structural Similarity Index $\text{SSI} = \frac{\text{Node Label Similarity} + \text{Edge F1 Score}}{2}$ ). These metrics differentiate structural and operational competencies, supporting continuous improvement in real-world, multi-task environments (Gabriel et al., 29 Oct 2024).
Applications: PAN AGI principles are applied in robotics (adaptive manipulation, error recovery, long-horizon task planning), multimodal AI agents for manufacturing (integrated LLM/MLLM architectures, reinforcement learning for proactive decision-making), collaborative multi-agent systems, and beyond (Ren et al., 2 Jul 2025).

7. Challenges and Future Directions

Energy Efficiency and Alignment: PAN AGI architectures explicitly address grand challenges of energy consumption and AI alignment by integrating energy-efficient hardware (GPUs, neuromorphic chips), multi-level moral/ethical modules, and feedback-based regulatory control throughout the system hierarchy (Kurshan, 2023).
Scalability and Robustness: Real-world deployment requires architectures that support distributed, federated learning, dynamic reasoning, and seamless integration of multimodal data, while maintaining stable performance and interpretability under continual learning and adaptation (Latapie et al., 2020, Sukhobokov et al., 11 Jan 2024).
Open Problems: Balancing exploration and exploitation, resolving architectural dilemmas (e.g., decoupling perception from memory), ensuring safety in nested agent hierarchies, and bridging gaps in cross-modal reasoning remain active research topics across PAN AGI systems (Miehling et al., 28 Feb 2025, Potapov et al., 2018, Wang et al., 20 May 2025).

Physical, Agentic, and Nested AGI systems synthesize insights from embodied cognition, agent-based modeling, hierarchical representation, and systems theory into a unified paradigm for general intelligence. Ongoing research converges on architectures that are rigorously grounded in physical reality, organized into hierarchical agentic modules, and deeply integrated for robust, adaptive intelligence across diverse environments and tasks.