Agentic Physical AI

• Agentic Physical AI is the integration of goal-directed reasoning with outcome-level physical validation, ensuring reliable real-world performance.
• It leverages architectures that combine large-scale neural models, multimodal perception, and physics-based policy optimization for robust closed-loop control.
• Demonstrated across domains like nuclear reactors, robotics, and network optimization, it achieves super-linear scaling and effective transfer in dynamic settings.

Agentic Physical AI encompasses a class of intelligent systems that couple high-level agentic reasoning with direct interaction and control over physical environments. These systems are characterized by goal-directed autonomy, the integration of large foundation models into perception–plan–act architectures, and closed-loop policies grounded in outcome-level, physics-based validation. Unlike perception-centric AI—which optimizes for semantic plausibility—Agentic Physical AI is constructed to guarantee execution-level success according to physical constraints, enabling its deployment in safety-critical, embodied, and dynamic real-world settings across domains from nuclear reactor control, scientific experimentation, and robotics, to infrastructure monitoring and network optimization (Lee et al., 29 Dec 2025, Lin et al., 3 Nov 2025, Hellert et al., 21 Sep 2025, Lykov et al., 17 Sep 2025, Sapkota et al., 8 Jun 2025, Pellejero et al., 4 Nov 2025, Yang et al., 29 May 2025).

1. Defining Agentic Physical AI: Principles and Distinctions

Agentic Physical AI is defined as the fusion of “agentic” and “physical” properties. Agentic refers to the capacity for learning a policy over multiple admissible control strategies and runtime selection to maximize task-level or closed-loop success, rather than regression to a single action label. Physical denotes the operationalization and verification of system correctness according to outcome-level rewards—such as simulation-based or real-world execution fidelity—rather than parameter- or imitation-based losses (e.g., rod position mean squared error) (Lee et al., 29 Dec 2025).

A central distinction from perception-centric models is that Agentic Physical AI optimizes for outcome-level guarantees under physics-constrained environments, answering “What actually works?” rather than “What seems right?”. This focus allows for policy stabilization, suppression of catastrophic tail risk, and non-trivial transfer across modalities, actuators, and operational domains. Benchmark evaluations (nuclear reactor control, robotic manipulation) demonstrate that Agentic Physical AI achieves closed-loop behaviors unattainable for general-purpose vision-LLMs, which are limited by input unfaithfulness and semantic approximation (Lee et al., 29 Dec 2025, Yang et al., 29 May 2025).

2. Core Architectures and Policy-Optimization Methodologies

Agentic Physical AI architectures leverage large-scale neural models (e.g., transformers, multimodal LAMs) that act within structured, policy-optimization loops. The defining workflow includes:

• Perception and State Representation: Multimodal inputs (numerical vectors, sensor streams, images) are encoded as unified state representations, either via engineered feature mappings, CNNs, or transformers (Lin et al., 3 Nov 2025, Sapkota et al., 8 Jun 2025).
• Agentic Reasoning and Planning: High-level tasks are decomposed into sub-goals or plans by large reasoning modules, using chain-of-thought LM prompting, workflow atlases, or SOP-derived templates. Example: Agentic Robot decomposes instructions into atomic subgoals, enabling modular execution (Yang et al., 29 May 2025).
• Policy Validation via Physics: Policy training objectives maximize expected physical reward through simulator-based or ground-truth evaluation. For reactor control, the policy $p_\theta(a|o^*)$ is trained to maximize the probability that the actuator mapping $\mathcal{F}(a)$ lands in an outcome band $\mathcal{B}_\delta(o^*)$ (±tolerance window) (Lee et al., 29 Dec 2025).
• Closed-Loop and Self-Verification: Temporal verifiers, explicit error metrics, and iterative re-planning loops ensure robust adaptation to errors during execution, as seen in frameworks like PhysicalAgent and Agentic Robot (Lykov et al., 17 Sep 2025, Yang et al., 29 May 2025).

System	Policy Objective	Verification Modality
Nuclear AI (Lee et al., 29 Dec 2025)	Execution-level, outcome band	Simulator: power tracking
Robot (Yang et al., 29 May 2025)	Subgoal completion & recovery	Visual buffer, temporal window checks
RAN (Pellejero et al., 4 Nov 2025)	KPI-driven RL, reflection	KPI time-series, closed-loop feedback

Physics-based validation, outcome-centric losses, and architecture-invariant policy transferability are recurring design motifs. In robotics, video diffusion models simulate candidate trajectories which are then scored and selected for execution, with iterative re-planning as the main error-recovery mechanism (Lykov et al., 17 Sep 2025).

3. Scaling Laws, Dataset Regimes, and Tail-Risk Suppression

Agentic Physical AI exhibits characteristic phase transitions and scaling phenomena distinct from classical models. In structured synthetic domains (e.g., nuclear reactor policy learning):

• Closed-loop success rates increase super-linearly with dataset scale, with a documented scaling exponent $\alpha(1\%) \approx 1.24$ for 1% tolerance (from 6% to 92% as data scale increases from $10^3$ to $10^5$) (Lee et al., 29 Dec 2025).
• Variance collapse is observed: terminal error variance reduces by >500× between small and large-scale models (e.g., 250 to 0.5); the 95th percentile error drops by ~30×, eliminating catastrophic outliers.
• Policies autonomously concentrate runtime action support, rejecting up to 70% of the original manifold in favor of empirically safer modes—evidenced by increased KL divergence $$D_{\text{KL}}(P_{\text{runtime}} \| P_{\text{train}})$$ (from 0.18 to 0.31 nats) as the model scale increases.

No online RL, reward shaping, or active safety constraints are needed; scaling structured, outcome-validated data with compact agentic models suffices for high-reliability behaviors (Lee et al., 29 Dec 2025).

4. Generalization, Transfer, and Multi-Domain Embodiment

Agentic Physical AI demonstrates multi-modal, cross-physics, and cross-embodiment transfer. Illustrative findings include:

• Nuclear AI: Phase 1 pretraining on “grammar” (control fields only) and subsequent LoRA-based conditioning enable policies to transfer to alternate physics benchmarks (e.g., PyRK point-kinetics), with >94% closed-loop success at ±1% tolerance without architectural modification (Lee et al., 29 Dec 2025).
• Robotics: Video-diffusion-based world models, in conjunction with lightweight adapters, achieve consistent performance across robotic embodiments (UR3 arms, Unitree humanoids, GR1 simulation) without statistically significant differences in median task success (Lykov et al., 17 Sep 2025).
• Human-AI Co-Embodiment: Agentic reasoning modules in mixed-reality wearable systems monitor, guide, and correct human operators in scientific/manufacturing settings—APEX achieves significant improvements in procedural accuracy (+53 percentage points over LLM baselines), tool recognition, and task completion times (Lin et al., 3 Nov 2025).
• Telecom and UAVs: Unified agentic frameworks with persistent memory, reflective goal decomposition, and multi-agent coordination generalize to network management (Pellejero et al., 4 Nov 2025) and aerial systems—enabling domain-agnostic application across collaborative robotics, logistics, and scientific infrastructure (Sapkota et al., 8 Jun 2025, Hellert et al., 21 Sep 2025).

These results are underpinned by the two-phase training curricula, modular design, and physics-based policy validation that characterize the agentic paradigm.

5. Application Domains and Quantitative Performance

Agentic Physical AI has been instantiated in various operational domains, each demonstrating dramatic improvements over traditional or generalist alternatives:

• Nuclear Reactor Control: 360M-parameter transformer policy achieves 97.4% success within ±5% error across control scenarios; severe (>10%) failures are eliminated without online adaptation (Lee et al., 29 Dec 2025).
• Laboratory Manufacturing (APEX): Step-tracking accuracy in cleanroom processes reaches ~92%, compared to 60% for the strongest LLM. Guided workflows halve task completion time for novice users (Lin et al., 3 Nov 2025).
• Robotic Manipulation: Agentic Robot attains a state-of-the-art 79.6% average success rate on LIBERO long-horizon tasks—a 6–8% absolute gain over competing architectures (Yang et al., 29 May 2025). PhysicalAgent’s iterative correction increases aggregate success from 20–30% on first attempts to 80% with multi-step recovery (Lykov et al., 17 Sep 2025).
• Large-Scale Experiments (ALS): Plan-first orchestration driven by LM planning at the ALS reduces machine-setup time from 180 minutes (manual) to 3 minutes (agentic), while strictly maintaining safety and reproducibility invariants (Hellert et al., 21 Sep 2025).
• Telecom RAN Automation: 5G-RAN agentic systems decrease anomaly-detection latency by ~30%, achieve 10% throughput uplift, and reduce misconfiguration rollbacks to <5% (Pellejero et al., 4 Nov 2025).
• Agentic UAVs: Reported field deployments indicate +30% reduction in chemical usage (agriculture), disaster-response swarms achieving 90% target recall in 10 minutes, and 96%+ true-positive rates in security domains—all with just-in-time collaborative planning (Sapkota et al., 8 Jun 2025).

6. Open Challenges, Limitations, and Future Directions

Although Agentic Physical AI achieves substantial advances in reliability, autonomy, and generalization, several open challenges remain:

• Real-World Dynamics and Latency: High computational cost (e.g., 20-30s/video rollout for PhysicalAgent) and sim-to-real transfer gaps, especially for nonrigid objects and noisy environments, limit on-device, real-time deployments. Techniques such as distillation, lightweight architectures, and adaptive scheduling are identified as ongoing research directions (Lykov et al., 17 Sep 2025, Yang et al., 29 May 2025).
• Explainability and Certification: The intrinsic opacity of foundation-model-driven policies, especially in regulated or high-risk domains (e.g., medical, nuclear), complicates interpretability, certification, and failure diagnosis (Sapkota et al., 8 Jun 2025).
• Regulatory, SWaP, and Energy Constraints: Physical platforms (UAVs, robots) face battery, payload, and onboard compute tradeoffs; infrastructural bottlenecks for edge AI and safety certification remain (Sapkota et al., 8 Jun 2025).
• Transfer and Scaling Boundaries: While hierarchical SOP decomposition and curriculum learning mitigate performance plateaus in longer workflows, memory pruning and sliding-scale retraining are essential for sustaining performance in large, complex domains (Lin et al., 3 Nov 2025).
• Going Beyond Static Policies: Real-time risk-aware decision support, multi-step procedures, mixed actuators, and full digital-twin integration (for simulation of candidate writes prior to hardware execution) are critical future milestones (Lee et al., 29 Dec 2025, Hellert et al., 21 Sep 2025).

7. Summary and Outlook

Agentic Physical AI introduces a domain-agnostic, outcome-validated paradigm for reliable goal-directed behavior in cyber-physical systems. Grounded in policy optimization via physics-based validation, it unifies multimodal perception, model-based planning, modular execution, and self-verification mechanisms. Its observed phase transitions under data scaling, autonomy in policy selection, and transfer across tasks, actuators, and physical simulators signify a structural shift from perception-centric imitation to robust, reliable, and context-adaptive control. Ongoing research targets the extension to longer-horizon procedures, larger actuator sets, and domains requiring stringent risk quantification and regulatory assurance (Lee et al., 29 Dec 2025, Lykov et al., 17 Sep 2025, Yang et al., 29 May 2025, Sapkota et al., 8 Jun 2025).