SANNet: Semantic-Aware Multi-Agent Networking

• SANNet is a semantic-aware networking paradigm that integrates high-level goal inference with cross-layer agent coordination for dynamic multi-agent systems.
• The framework decomposes user intents into subtasks managed by application, physical, and network agents to address conflicting objectives via adaptive optimization.
• Experimental results and hardware prototypes validate its dynamic weighting strategy, improving QoE while ensuring robust, scalable operation in real-world environments.

Semantic-Aware Multi-Agent Networking (SANNet) is an advanced networking paradigm that integrates semantic reasoning, cross-layer agent coordination, and conflict resolution to enable autonomous, adaptive, and efficient multi-agent system operation in dynamic environments. Unlike conventional AI networking approaches, SANNet is fundamentally designed to infer high-level user intent, translate it into actionable goals for heterogeneous agents across different system layers, and orchestrate their collaboration—even in the presence of conflicting objectives. This framework provides theoretical guarantees on multi-agent coordination and generalization and has been validated through both experimental studies and hardware prototypes.

1. Architecture and System Components

SANNet features a layered agentic architecture with explicit semantic goal inference and dynamic multi-agent control. The core elements are:

• Application-Layer Agent (aAgent): Interfaces with users, captures prompts (e.g., via natural language) or sensory input, and relays them to the agent controller. Employs LLMs (e.g., Qwen-7B) for understanding and goal extraction.
• Physical-Layer Agent (pAgent): Interfaces with radios/physical channel; responsible for real-time spectrum sensing, channel metrics gathering, and radio environment interaction.
• Network-Layer Agent (nAgent): Manages network resources, including routing, bandwidth, and Quality of Service (QoS) control.

A central agent controller acts as an orchestrator. Upon receiving a semantic demand, it decomposes the goal into a vector of subtasks mapped to the respective agents. Each agent publishes an "agent card" specifying its function, state, action space, and loss function. The controller coordinates multi-agent collaboration and resolves cross-layer conflicts by dynamically adjusting inter-agent weighting in the optimization process.

The system architecture is mathematically formalized as a multi-objective minimization:

$$\text{P1:} \quad \min L_m(\cdot) := \langle l_m^a(\cdot),\, l_m^p(\cdot),\, l_m^n(\cdot) \rangle$$

where $l_m^a$, $l_m^p$, $l_m^n$ are the application, physical, and network layer-specific loss functions. The controller seeks Pareto optimality across these objectives.

Figure_SANNetModel (referenced in the paper) demonstrates this agentic architecture, including the communication patterns and cross-layer agent controller logic (Xiao et al., 25 May 2025).

2. Semantic Goal Inference and Task Decomposition

Semantic goal inference in SANNet is effected by the aAgent and the agent controller using advanced natural language processing. The inference process flow is:

1. User Prompt Acquisition: The aAgent detects user input—e.g., "increase video resolution" or "reduce latency."
2. Semantic Parsing: The agent controller processes the prompt and infers an explicit semantic goal.
3. Subtask Decomposition: The semantic goal is decomposed into subtasks, each mapped to a distinct system layer (application, physical, network).
4. Agent Assignment: Based on the parsed subtasks, relevant agents are selected from their published agent cards and engaged in collaborative optimization.

This semantic-aware translation ensures that agents are not reactive to mere technical signals but to the interpreted meaning behind user or system inputs, enabling more intelligent resource coordination and dynamic adaptation.

3. Conflict-Resolving Multi-Agent Optimization

A central challenge in multi-agent networking is that agents often have conflicting objectives—for example, maximizing application-layer QoE may require increasing video resolution, while the physical-layer agent may prioritize lowering spectral occupancy under poor channel conditions.

SANNet introduces a dynamic weighting-based conflict-resolving mechanism. Instead of static weighting of layer-wise gradients, the controller adaptively updates each agent’s gradient weight $\gamma^i$ at every iteration to achieve Pareto efficiency. The update rules are

$$\gamma_{t+1}^i = \gamma_t^i - \eta_t \nabla l^i(\omega_t, \gamma_t^i, d_{t,1}^i)^T \nabla l^i(\omega_t, \gamma_t^i, d_{t,2}^i)$$

$$\omega_{t+1} = \omega_t - \beta_t \nabla l^i(\omega_t, \gamma_{t+1}^i, d_{t,3}^i)$$

where $l^i$ is the layer-specific loss and $d_{t,j}^i$ are sampled data points. Algorithm 1 in the paper details the parallel, asynchronous update strategy.

This approach ensures that the direction of collective optimization is continuously adapted so that the multi-agent system converges to a solution that respects the trade-offs between often incompatible objectives.

4. Theoretical Guarantees on Coordination and Generalization

SANNet provides explicit theoretical guarantees on two key performance metrics:

• Conflict Error (C-error): Quantifies the deviation from gradient alignment (i.e., the extent and impact of multi-agent objective conflicts). The paper proves (Theorem 1) that the average C-error decreases at a rate $O(T^{-1/4})$ with iteration count $T$:

$$EC \leq \frac{4}{\eta T} + 6\sqrt{3 \ell_F^\prime \ell_F^2 \frac{\beta}{\eta} + 3\eta \ell_F^4}$$

with Lipschitz constants $\ell_F$, step sizes $\eta$, $\beta$.

• Generalization Error (G-error): Bounds the discrepancy between empirical model gradients (from training data) and the expected gradients over real-world distributions. For each agent $i$:

$$G^i = \|\nabla l^i(\omega^i, \text{data}) - \nabla \tilde{l}^i(\omega^i)\|$$

$$G = \|\sum_i \gamma^i (\nabla l^i - \nabla \tilde{l}^i)\|$$

Theorem 2 provides an explicit, sample-size dependent upper bound ensuring that as the training set grows, G-error diminishes appropriately. Together, these results demonstrate SANNet’s robustness to conflicting objectives and its generalization capability in dynamic, non-stationary environments.

5. Experimental Results and Real-World Prototype

Empirical evaluation demonstrates marked improvements in coordination and performance. In immersive communication scenarios:

• The aAgent detects and semantically interprets user dissatisfaction.
• Real-time adjustments are made: Unity-based renderers change video resolution, the pAgent adapts modulation/coding or channel assignment, and the nAgent manages bandwidth.
• Dynamic weighting–based optimization reduces C-error by up to 63% compared to static weighting—a substantial improvement in balancing cross-layer objectives.
• While decreasing C-error, G-error increases only marginally, preserving generalizability.

Prototype validation includes:

• A hardware-in-the-loop testbed based on open RAN and commercial 5GS core.
• User equipment implemented via software-defined radio (NI 2944R USRP); the gNB is equipped with Intel i9 and RTX 4090 GPU.
• srsRAN is used for RAN emulation, and transformer-based LLMs and prediction models for agent logic.
• Results confirm improved Quality of Experience (QoE) and system adaptability under user-driven semantic goals.

6. Advanced Features, Implications, and Future Directions

SANNet’s design offers several advanced properties:

• Cross-layer integration: Semantic inference and conflict resolution span application, network, and physical layers for holistic adaptation.
• Agentic modularity: Agents are self-documenting (via agent cards) and independently operable, supporting plug-and-play extensibility.
• Automatic goal discovery and assignment: By detecting semantic intent, the framework supports fully autonomous, intention-driven system management and multi-agent orchestration.
• Scalability and resource efficiency: Dynamic conflict-resolution and agent assignment enable robust operation in large, heterogeneous systems and time-varying environments.

Future directions alluded to include:

• Extending to more complex agent populations and networking domains (e.g., industrial IoT, autonomous driving).
• Incorporation of additional agent layers (e.g., energy management, edge orchestration).
• Integration with knowledge-driven or context-aware semantic models for enhanced decision-making.
• Ongoing advancements in LLM-based semantic parsing and goal inference to broaden the spectrum of user commands and environmental stimuli that can be effectively handled.

Conclusion

SANNet is a semantic-aware agentic AI networking framework combining semantic goal inference, flexible task decomposition, and theoretical conflict-resolving optimization. Its layered architecture and dynamic weighting strategy enable scalable, robust, and adaptive coordination across application, network, and physical system layers, even in the presence of inter-agent conflicts and dynamic operating conditions. Theoretical guarantees, validated by experiments and hardware prototypes, suggest that SANNet is a foundational design for the next generation of autonomous, self-optimizing multi-agent networking systems (Xiao et al., 25 May 2025).