AI-Native Internet

• AI-Native Internet is a reimagined network framework that integrates AI into every layer, enabling pervasive intelligence and dynamic service orchestration.
• It employs advanced methodologies like federated learning, semantic web protocols, and quantum-enabled optimization to enhance data governance and network performance.
• Architectural innovations, including agentic protocols and AI-driven orchestration, ensure secure, scalable, and adaptive network operations for future Internet applications.

An AI-Native Internet constitutes a clean-slate reimagining of Internet architecture, protocol stack, and operational paradigms to treat artificial intelligence as a pervasive substrate rather than a supplementary application layer. Instead of merely embedding AI-powered analytics within legacy infrastructure, the AI-Native Internet incorporates deep integration of intelligence at every plane: data governance, resource orchestration, protocol negotiation, semantic information retrieval, service composition, and inter-agent collaboration. This endows the network itself with capabilities for “intelligence inclusion”—allowing intelligence services to be accessed by anyone, at anytime, and anywhere, supported by network-wide, cross-domain compute, storage, and AI workflow orchestration (Wu et al., 2021), and catalyzed by advances in agentic protocols, semantic web substrates, and federated learning across diverse physical, virtual, and quantum-enabled networks.

1. Architectural Foundations and Functional Planes

The reference architecture of the AI-Native Internet, as synthesized in (Wu et al., 2021), is anchored in a four-plane, end-to-end system stack:

• Network Function Plane: Delivers deeply converged communication and computing capabilities across RAN, core, and transport. It supports dynamic, task-oriented connectivity and elastic instantiation of service slices. Components include cNB (control node), sNB (service node), and CmP (computing plane).
• Independent Data Plane: Provides unified, end-to-end data governance that unifies telemetry, business, vertical, and terminal data under regulatory constraints (e.g., GDPR) and privacy-preserving frameworks. Data flow models use M/M/1 queueing formulations, with event arrival rate $\lambda$ and service rate $\mu$ ensuring $\lambda < \mu$ for stability, and throughput bounded by the minimum capacity or processing rate across pipeline stages.
• Intelligent Plane: Constitutes the logical workflow and lifecycle management for AI services, including AI service orchestration, infrastructure mapping, and accuracy/latency optimization. Logical workflows are modeled as DAGs $W(V, E)$, with modules $v \in V$ assigned to compute nodes subject to capacity constraints ($\sum_{v \to n} c_v \leq C_n$).
• Everything-as-a-Service (XaaS) Platform: Materializes infrastructure (IaaS), platform (PaaS), and application (SaaS) services, exposing AI primitives, CI/CD tooling, and marketplaces to operators, third-party providers, and end users.

The orchestrated synergy among these planes enables converged connectivity and computing, real-time workflow orchestration, federated data management, and on-demand service composition (Wu et al., 2021).

2. Protocols, Semantic Web, and Agentic Interconnection

AI-Native Internet protocols depart from legacy, human-centric interfaces and adopt architectures designed for autonomous agent performance, identity authentication, dynamic capability negotiation, and native semantic interoperability (Chang et al., 18 Jul 2025, Raskar et al., 18 Jul 2025). In particular:

• Agent Network Protocol (ANP) (Chang et al., 18 Jul 2025): Defines a three-layer protocol system:
  • Identity & encrypted communication layer (DID-based, “did:wba” scheme; ECDHE key exchange, LaTeX: $S = d_A \times Q_B$ with session keys $$K = \mathrm{KDF}(S \,\|\, \mathrm{DID}_A \,\|\, \mathrm{DID}_B)$$).
  • Meta-protocol negotiation layer: state-machine for proposal, response, acceptance, and capability matching ($C_{\mathrm{agree}} = (C_A \cap C_B) \cap R$).
  • Application protocol layer: semantic, schema-driven service descriptions (JSON-LD), exposing capabilities and endpoints for discovery.
• NANDA Index & AgentFacts (Raskar et al., 18 Jul 2025): Implements a quilt-like, horizontally scalable registry for agent discovery, authentication, and capability assertion, utilizing rapid propagation (sub-second), schema-validated JSON-LD and W3C Verifiable Credential chains, CRDT-based metadata updates, and privacy-preserving lookup (Tor/IPFS relays, ZKP selective disclosure).
• Semantic Web Substrate (Bilal et al., 23 Nov 2025): Web-native semantic resolvers supersede document-centric HTML. Servers expose pre-chunked, vectorized units; global resolvers map queries $Q$ to sources $S$ and retrieve contextually relevant chunks $C$. Formal retrieval: $R_1: Q \rightarrow 2^S$, $R_2,s: Q \rightarrow 2^C$, with similarity metrics $$sim(q, c) = \langle f(q), f(c) \rangle / (\|f(q)\| \cdot \|f(c)\|)$$. Motivational experiments indicate 74%–87% bandwidth reduction at parity of answer quality.

3. Dynamic Control, Interoperability, and Autonomous Orchestration

AI-Native Internet control is achieved through:

• On-demand AI-native interface generation (Dandekar et al., 21 Aug 2025): LLM-based multi-agent frameworks synthesize dynamic control interfaces between network functions (NFs), bypassing rigid, vendor-specific standards. Matching and code-generation agents employ retrieval-augmented generation (RAG) and semantic similarity optimization, accelerating interoperability and zero-touch provisioning. Trade-offs are quantified: sub-10 s provisioning via GPT-4o, sub-100 s via Llama3.3.
• Agentic orchestration architectures (Zhani et al., 2 Sep 2025): Continually self-evolving LLM-based agents are instantiated at every architectural layer (application, control, data). They collaboratively analyze requirements, optimize resource allocation (multi-objective cost: $$\mathrm{Obj} = \alpha_1 \cdot \mathrm{Cost} + \alpha_2 \cdot (1-\mathrm{Profit}) + \alpha_3 \cdot [\mathrm{Utilization} + (1-\mathrm{FairnessIndex})] + \alpha_4 \cdot \mathrm{GreenPenalty}$$), synthesize new protocols and SFCs, refine congestion control (Linux pluggable modules), and adapt to traffic surges with dynamic reweighting. Experiments confirm improvements in throughput, delay, loss, and sustainability.
• AI-Native Network Slicing (Wu et al., 2021): Slicing is both managed by AI (slice admission, VNF placement, real-time resource orchestration) and provisioned to support AI workloads (algorithm, training mode, reserved resources). RL-based algorithms operate across preparation, planning, and operation phases optimizing resource reservation and SLA compliance. Formulations solve discounted MDPs and actor–critic reinforcement learning instances with queuing constraints $$E[delay(r_t, \lambda_t)] = 1/(\mu(r_t) - \lambda_t) \leq D_{req}$$.

4. Data Governance, Sovereignty, and Security

Ensuring robust data governance, sovereignty, and security is intrinsic (Wu et al., 2021, Chetty et al., 8 Sep 2025, Katsaros et al., 11 Nov 2024):

• Unified Data Plane: Implements centralized policy enforcement, desensitization, and access API. Models data ingestion as queueing systems and applies throughput constraints across pipeline stages.
• Sovereign AI (Chetty et al., 8 Sep 2025): Operators retain jurisdictional control over AI life-cycle, operation, and compliance; xApps/rApps in Near-/Non-RT RICs execute under policy-aligned control, cryptographic signing, rollback, and secure federated learning. FL aggregation leverages robust algorithms (e.g., geometric median: $w_{t+1} = \arg\min_w \sum_{i \in S} \|w - w_i\|^2$).
• End-to-End Security and Trust (Katsaros et al., 11 Nov 2024, Raskar et al., 18 Jul 2025): Blockchain, distributed ledgers, privacy-preserving credentials, anomaly detection (autoencoders), and explainable AI are standard. Revocation and key rotation are performed in sub-second scales, and privacy-preserving protocol support least-disclosure queries and mixnet path selection.

5. Distributed Intelligence, Federated Learning, and Quantum Integration

AI-Native Internet mandates scalable, distributed intelligence pipelined through federated and quantum-enabled learning mechanisms (Shaon et al., 9 Sep 2025, Chen et al., 2023):

• Federated Learning Workflows (Wu et al., 2021, Shaon et al., 9 Sep 2025): Model training occurs across $K$ clients over $T$ rounds; total time: $$T_{\text{train}} = \sum_{t=1}^T (\max_k t^{local}_k + t^{upload}_t + t^{download}_t)$$. Quantum Federated Learning (QFL) with QAOA accelerates global convergence ($$E[L(\theta_{QFL}^{(T)})] - L^* \leq O(\frac{1}{\sqrt{N T}})\frac{1}{\sqrt{s_q}}$$), enhances privacy via QKD, and boosts channel spectral efficiency using superdense coding ($R_k^{Q} = B \log_2(1 + SNR_k) + \log_2 d$).
• Cloud-Edge-End Collaboration Framework (Chen et al., 2023): Hierarchical deployment of customized PFMs, expert knowledge graphs, and adaptive task toolkit (TOAT). Orchestration uses intent recognition, graph-based workflow decomposition, and multi-agent RL for inferencing and resource management. Mathematical application: sum-rate maximization in massive MIMO via PFM-driven orchestration yields adaptive algorithm choice and computational cost reduction with strict latency guarantees.

6. Performance Metrics, Optimization Formulations, and Practical Evaluations

Operationally, AI-Native Internet performance is characterized by:

• Latency and Reliability (Wu et al., 2021): E2E latency bound for $H$ hops with M/M/1 queues: $$L_{e2e} \leq \sum_{h=1}^H \frac{1}{\mu_h - \lambda}$$; reliability: $R_{e2e} = \prod_{h=1}^H R_h$.
• AI Inference Accuracy vs. Delay: $A(D) = A_0 - \kappa D$ (stale model effect).
• Resource Optimization: Task graph allocation and scaling solve: $\max_x B(x) - \lambda C(x)$ (diminishing returns); network functions optimize $$\max \sum_\tau u_\tau(\mathrm{throughput}_\tau, \mathrm{latency}_\tau)$$ with resource constraints.
• Benchmarks: Real-world deployments such as NVIDIA AI Aerial demonstrate +40%–58% throughput gain, 3–5 dB MSE reduction, and <1 ms per-slot ML inference latency (Cohen-Arazi et al., 2 Oct 2025). Simulations confirm quantitative benefits in SFC placement and multi-domain orchestration (Katsaros et al., 11 Nov 2024).

Metric	Formula/Result	Reference
Data Ingestion Stability	$\lambda < \mu$ (M/M/1)	(Wu et al., 2021)
E2E Latency	$L_{e2e} \leq \sum_{h=1}^H {1}/{(\mu_h-\lambda)}$	(Wu et al., 2021)
FL Training Time	$$T_{\text{train}} = \sum_{t=1}^T (\max_k t^{local}_k + t^{upload}_t + t^{download}_t)$$	(Wu et al., 2021)
Quantum FL Convergence	$O(\frac{1}{\sqrt{N T}}) / \sqrt{s_q}$	(Shaon et al., 9 Sep 2025)
Multi-objective SFC Placement	$$\min_{x} \alpha \sum_{f} \mathrm{delay}_f(x) + \beta \sum_v E_v(x)$$	(Katsaros et al., 11 Nov 2024)
Throughput Gain (CNN/DSP)	+40%–58% (real/virtual lab)	(Cohen-Arazi et al., 2 Oct 2025)

7. Open Challenges and Research Directions

Current efforts chart foundational advances and unresolved challenges, including:

• Data Sovereignty, Privacy, and Compliance: Fine-grained slicing, dynamic consent, enforcement of regulatory and jurisdictional boundaries (Wu et al., 2021, Chetty et al., 8 Sep 2025).
• Cross-Plane, Cross-Domain APIs and Standardization: Robust, zero-latency interfaces for AI orchestration, secure federated learning, inter-agent protocols, and semantic domain interoperability (Dandekar et al., 21 Aug 2025, Bilal et al., 23 Nov 2025).
• Real-Time Orchestration and Heterogeneous Model Lifecycle: Sub-millisecond decision loops, support for diverse AI paradigms, explainability, and trustworthiness (Wu et al., 2021).
• Security and Trust: Quantum-safe cryptography, model poisoning defenses, distributed ledgers for provenance and auditability (Chetty et al., 8 Sep 2025, Katsaros et al., 11 Nov 2024).
• Economic Models and Multi-Stakeholder Governance: Pricing, SLA management, incentives for semantic web publishing and agent registry hosting (Bilal et al., 23 Nov 2025, Raskar et al., 18 Jul 2025).

A plausible implication is that large-scale deployment will demand coordinated progress across agent identity frameworks (NANDA/ANP), federated and quantum ML pipelines, adaptive protocol stacks, and holistic governance spanning technical, regulatory, and economic dimensions. The AI-Native Internet is therefore evolving toward a highly modular, secure, and continuously intelligent substrate—sustaining lifelong learning, semantic interoperability, and dynamic agent orchestration on a global scale.