AI Agents Tune In: Smarter Networks Through Integrated Sensing and Communication

Author: Denis Avetisyan

This review explores how intelligent AI agents, powered by large language models and reinforcement learning, are optimizing the synergy between communication and environmental sensing for next-generation networks.

Intelligent Sensing, Actuation, and Control (ISAC) systems-architected as either Reactive Control Center (RCC) or Distributed Feedback Reactive Control (DFRC)-are increasingly deployed across diverse applications including smart homes, intelligent factories, and vehicle networks, and the integration of agentic AI promises to further enhance their autonomy and intelligence within these complex environments.

The paper analyzes a framework leveraging agentic AI, including Mixture of Experts, to enhance performance in both communication rate and sensing accuracy within integrated sensing and communication (ISAC) systems.

As wireless environments grow increasingly complex, traditional methods struggle to maintain efficiency in integrated sensing and communication (ISAC) systems. This paper, ‘Agentic AI for Integrated Sensing and Communication: Analysis, Framework, and Case Study’, investigates the potential of agentic AI-leveraging large language models, deep reinforcement learning, and mixture of experts-to address these challenges. Our findings demonstrate that this approach significantly optimizes ISAC performance, improving both communication rates and sensing accuracy. Could agentic AI unlock a new era of truly autonomous and intelligent wireless networks?

The Illusion of Autonomy: Why We Need More Than Just Clever Algorithms

Contemporary artificial intelligence frequently encounters limitations when tasked with problems demanding intricate, sequential thought processes and independent judgment. Existing systems, while proficient in narrow applications, often falter as the number of required steps increases or when faced with unforeseen circumstances. This stems from a reliance on pattern recognition within fixed datasets, hindering their ability to generalize knowledge or adapt to novel situations effectively. Consequently, even seemingly simple tasks requiring planning, resource management, or causal inference can prove challenging, highlighting a critical gap between current capabilities and truly autonomous intelligence. The inability to reliably navigate complexity underscores the need for innovative approaches that move beyond static responses toward dynamic, reasoned action.

Agentic AI represents a fundamental departure from traditional artificial intelligence, moving beyond systems designed for specific tasks to create entities capable of autonomous operation within unpredictable settings. These systems don’t simply respond to stimuli; they perceive their environment through sensors or data streams, reason about goals and potential actions, and then act to achieve desired outcomes – all without constant human intervention. This necessitates a shift in architectural design, prioritizing adaptability and the ability to handle unforeseen circumstances, effectively allowing AI to navigate complexity and formulate strategies in real-time. The promise of agentic AI lies in its potential to tackle problems requiring prolonged interaction with the world, from robotic exploration and personalized healthcare to automated scientific discovery and dynamic resource management, offering a level of flexibility and intelligence previously unattainable.

The emergence of agentic AI demands a fundamental shift in architectural design, moving beyond current models that often falter when confronted with extended reasoning or intricate scenarios. Traditional AI frequently processes information in isolated snapshots, hindering its ability to maintain context across multiple steps or to effectively navigate environments defined by numerous interacting elements. Novel architectures are therefore being developed to incorporate mechanisms for long-term dependency handling – essentially, a ‘memory’ that retains and utilizes past information – and to model complex interactions, perhaps through hierarchical structures or graph-based representations. These systems strive to not only process data, but to understand relationships, anticipate consequences, and adapt strategies over extended periods, enabling truly autonomous behavior in dynamic and unpredictable settings. This necessitates innovations in areas such as recurrent neural networks with attention mechanisms, transformer networks capable of processing sequential data, and reinforcement learning algorithms designed to optimize for long-term rewards in complex environments.

This agentic ISAC framework utilizes a Transformer-based MoE for reasoning and action selection, leveraging external tools and an autonomously designed reward function to evaluate performance within a given environment.

Transformers: The Least-Bad Way to Pretend We’ve Solved Long-Term Memory

The Transformer architecture provides a robust framework for agentic systems by addressing limitations in modeling long-term dependencies, a challenge for recurrent neural networks (RNNs). Unlike RNNs which process sequential data step-by-step, potentially losing information over long sequences, Transformers utilize self-attention mechanisms to weigh the relevance of each input element to all others. This parallel processing capability, combined with positional encodings to retain sequence order, allows the model to capture relationships between distant elements within a sequence without the vanishing gradient problem inherent in RNNs. Consequently, Transformers enable agents to maintain context and reason effectively over extended interactions and complex state spaces, improving performance in tasks requiring memory and sequential understanding.

Transformer-based agents utilize attention mechanisms to selectively process input data, assigning varying weights to different elements based on their relevance to the current task. This process allows the agent to prioritize information, effectively focusing on the most pertinent details while downplaying irrelevant noise. Specifically, the attention mechanism calculates a weighted sum of input embeddings, where the weights are determined by the similarity between a query vector and key vectors associated with each input element. This dynamic weighting enables the agent to capture complex relationships and dependencies within the input, ultimately improving the quality and efficiency of its decision-making process by reducing computational load on less important data.

Evaluations of the Transformer-based framework demonstrate a 131.25% improvement in communication rate when contrasted with conventional methods. This performance gain is particularly significant in real-time applications where efficient data transmission is critical. The increased rate allows for faster exchange of information between agents or components within a system, leading to quicker response times and improved overall system performance. Testing has shown this improvement is maintained across varying data complexities and network conditions, suggesting robust applicability in dynamic environments.

Simulation results demonstrate that the agentic ISAC framework effectively designs reward functions from system models and achieves communication rates approaching the Cramer-Rao Bound (CRB) with varying base station (BS) transmit power.

Differential Privacy: Because We Pretend to Care About User Data

Differential privacy is a statistical technique used to enable analysis of datasets while limiting the exposure of individual-level data. It operates by adding a controlled amount of random noise to queries or results, ensuring that the presence or absence of any single data point has a limited effect on the outcome. This is typically quantified using the parameter $\epsilon$, with lower values indicating stronger privacy guarantees but potentially reduced data utility. In the context of agentic AI systems, which often rely on large datasets for training and operation, differential privacy is crucial for complying with data protection regulations and building user trust by mitigating the risk of re-identification or inference of sensitive personal information.

Differential privacy achieves data anonymization by introducing a controlled amount of random noise during data processing. This noise, calibrated to the sensitivity of the query and the desired privacy level (typically expressed as $\epsilon$ and $\delta$), obscures individual contributions while preserving the overall statistical properties of the dataset. The magnitude of the added noise is carefully determined; sufficient noise protects against re-identification attacks, while minimal noise ensures the utility of the analytical results remains acceptable. Techniques like Laplace or Gaussian mechanisms are commonly employed to generate this noise, and composition theorems allow for bounding the cumulative privacy loss across multiple queries. This process enables researchers and developers to perform meaningful data analysis without directly accessing or revealing personally identifiable information.

The integration of differential privacy with agentic AI systems enables responsible data utilization by mitigating the risk of revealing personally identifiable information during autonomous operation. Agentic AI, capable of independently pursuing goals, frequently requires access to sensitive datasets; applying differential privacy ensures that data analysis conducted by these agents does not compromise individual privacy. This is achieved by adding statistical noise to queries or datasets, guaranteeing that the output remains largely consistent even with slight modifications to individual records. Consequently, organizations can leverage the power of agentic AI for data-driven tasks while simultaneously demonstrating a commitment to data protection, which is crucial for building and maintaining public trust in these increasingly prevalent technologies.

Blockchain: Because We Need a Place to Blame the AI When Things Go Wrong

Agentic AI, capable of autonomous action, demands robust systems for tracking and verifying its decisions. Blockchain technology offers precisely this, functioning as a distributed, immutable ledger that meticulously records each action undertaken by the AI. Every transaction, data access, or decision point is cryptographically sealed and appended to the chain, creating a transparent history accessible for review. This isn’t merely a log; the decentralized nature of blockchain eliminates single points of failure or manipulation, ensuring the integrity of the record. Consequently, stakeholders can confidently verify the AI’s behavior, fostering trust and enabling accountability in increasingly complex autonomous systems, and providing a clear audit trail for any investigation or dispute.

The core benefit of employing blockchain technology alongside agentic AI lies in the creation of an unalterable decision history. Each action taken by the AI, and the data informing that action, is recorded as a transaction on the blockchain, forming a permanent and verifiable audit trail. This immutability is crucial; it prevents malicious or accidental tampering with the record, ensuring the integrity of the AI’s reasoning process. Consequently, stakeholders can confidently reconstruct why an AI made a specific choice, identify potential biases, and validate compliance with pre-defined rules or ethical guidelines. This level of transparency isn’t simply about accountability; it’s about building a robust system where trust isn’t assumed, but demonstrably proven through cryptographic verification of every decision made by the autonomous agent.

The convergence of blockchain technology and agentic artificial intelligence fosters a new paradigm of accountability within autonomous systems. By recording each action and decision onto a distributed, immutable ledger, blockchain establishes a verifiable trail of an AI agent’s behavior. This transparency is critical for building trust, as stakeholders can independently audit the reasoning and outcomes of complex AI operations. Consequently, the integration mitigates concerns surrounding ‘black box’ algorithms and promotes responsible AI development, particularly in high-stakes applications where explainability and redress are paramount. The resulting system isn’t simply about tracking what an AI did, but why, offering a foundation for demonstrable integrity and fostering wider societal acceptance of increasingly autonomous agents.

Reward Engineering: The Art of Telling the AI What We Think We Want

Reward function design stands as a foundational element in the development of agentic artificial intelligence, effectively serving as the system’s primary directive. This function doesn’t simply offer encouragement; it meticulously defines the agent’s goals and objectives, translating desired behaviors into quantifiable signals. The sophistication of this design directly impacts the agent’s learning process and ultimate performance; a poorly constructed reward function can lead to unintended consequences or suboptimal outcomes, while a well-crafted one guides the agent towards achieving complex tasks with precision and efficiency. Consequently, significant research focuses on techniques for specifying these functions, exploring methods ranging from manual engineering to learning them directly from human preferences, all to ensure the agent’s actions consistently align with intended purposes and broader ethical considerations.

The successful integration of agentic AI hinges on crafting reward functions that not only incentivize desired behaviors but also ensure alignment with human values. These functions serve as the primary communication channel between humans and artificial intelligence, dictating what constitutes success for the agent. A thoughtfully designed reward system guides the agent towards outcomes that are genuinely beneficial, preventing unintended consequences or the pursuit of goals detrimental to human interests. This necessitates careful consideration of potential side effects and a holistic approach to defining ‘good’ behavior, moving beyond simple task completion to encompass ethical considerations and long-term societal impact. Ultimately, the robustness and trustworthiness of agentic systems are inextricably linked to the precision and thoughtfulness with which these reward structures are established.

Recent advancements in agentic AI have yielded quantifiable improvements in performance metrics, notably demonstrated by the agentic ISAC framework. This system achieved a 5.43% reduction in the Cramér-Rao Bound (CRB) when contrasted with conventional methodologies. The CRB, a fundamental concept in statistics, establishes a lower limit on the variance of an estimator; therefore, a reduction signifies enhanced precision and reliability in the agent’s decision-making process. This improvement isn’t merely statistical, but translates to a more accurate and dependable system capable of consistently achieving desired outcomes, particularly crucial in complex, real-world applications where even small errors can have significant consequences. The framework’s success highlights the potential for statistically-driven design to optimize agentic behavior and push the boundaries of AI performance.

The pursuit of agentic AI for ISAC, as detailed in this work, feels predictably ambitious. It’s a complex system layered atop another – LLMs guiding DRL within the constraints of MoE – and one can anticipate the inevitable accrual of technical debt. This isn’t a criticism, merely an observation that elegant architectures, however promising in simulation, will always encounter the brutal realities of production environments. As David Hilbert famously stated, “We must be able to answer the question: what are the ultimate foundations of mathematics?” The same applies here; the ultimate foundation of any AI system is its ability to function reliably when faced with unforeseen circumstances. The paper’s focus on reward function design highlights this implicitly; crafting a reward function comprehensive enough to cover all edge cases is an exercise in futility. It’s a temporary reprieve, not a solution.

What’s Next?

The pursuit of agentic AI for ISAC, as demonstrated, merely shifts complexity. The current architecture – LLMs guiding DRL, augmented by MoE – offers incremental gains, but introduces a new vector of fragility. Reward function design remains the fundamental bottleneck; a beautifully optimized agent is useless if its incentives are misaligned with real-world performance, especially when deployed beyond controlled simulations. The paper highlights performance metrics, yet sidesteps the inevitable entropy of production environments.

Future work will undoubtedly focus on scaling these models, increasing parameter counts in a desperate attempt to ‘solve’ the problem with brute force. This is a familiar pattern. The field needs less architectural novelty and more rigorous analysis of system-level behavior. The question isn’t whether an agent can optimize ISAC, but whether the cost of maintaining that optimization – the debugging, the retraining, the constant recalibration – outweighs the benefits.

Ultimately, the promise of truly ‘intelligent’ ISAC will remain elusive until the field acknowledges a simple truth: we don’t need more microservices – we need fewer illusions. The real challenge isn’t building agents that appear to reason, but designing systems that are inherently robust, observable, and predictable, even – or especially – when things go wrong.

Original article: https://arxiv.org/pdf/2512.15044.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/