Smart Networks: AI Agents Powered by External Tools

by

Author: Denis Avetisyan

This review explores how equipping artificial intelligence with the ability to utilize external tools is unlocking new capabilities in next-generation communication networks.

The progression from simple tooling to complex systems represents not construction, but cultivation-a deliberate increase in systematization designed to address increasingly nuanced questions, ultimately yielding a complete, self-sustaining ecosystem for agentic AI in communication-a prophecy of emergent behaviors rather than a blueprint for control.
The progression from simple tooling to complex systems represents not construction, but cultivation-a deliberate increase in systematization designed to address increasingly nuanced questions, ultimately yielding a complete, self-sustaining ecosystem for agentic AI in communication-a prophecy of emergent behaviors rather than a blueprint for control.

The integration of tool intelligence with large language models enhances agentic AI performance, demonstrated through advancements in UAV trajectory planning using reinforcement learning.

While large language models demonstrate remarkable reasoning abilities, their potential remains limited by an inability to directly interact with the physical world. This paper, ‘Unleashing Tool Engineering and Intelligence for Agentic AI in Next-Generation Communication Networks’, addresses this gap by exploring how equipping LLMs with external tools can create truly autonomous agents for advanced communication tasks. We present a systematic review of tool engineering – from creation to benchmarking – and demonstrate improved UAV trajectory planning via a teacher-guided reinforcement learning approach. Could this paradigm of tool-augmented intelligence unlock the full potential of agentic AI in the 6G era and beyond?

The Inevitable Fracture: When Intelligence Meets Contingency

Conventional artificial intelligence systems often falter when confronted with the ambiguities and constant shifts inherent in real-world scenarios. These systems, typically designed for narrowly defined tasks, lack the flexibility to adjust to unforeseen circumstances or integrate new information effectively. A robot programmed to assemble a product on a static conveyor belt, for instance, might be rendered useless by a slight alteration in the assembly line or the introduction of a new component. This rigidity stems from a reliance on pre-programmed rules and a limited capacity for generalization; traditional AI excels at pattern recognition but struggles with true understanding and adaptive behavior, hindering its application in dynamic, unpredictable environments that demand continuous learning and interaction.

Agentic AI represents a significant departure from conventional artificial intelligence systems, positioning Large Language Models (LLMs) not merely as data processors, but as the central cognitive engine driving autonomous behavior. Traditionally, AI required explicit programming for every conceivable scenario; agentic systems, however, utilize the reasoning capabilities embedded within LLMs to dynamically assess situations, formulate plans, and execute actions without constant human intervention. This approach allows for adaptability in complex, real-world environments where pre-defined rules are insufficient, enabling the AI to pursue goals through iterative problem-solving and tool utilization. The shift emphasizes reasoning as the foundational element for action, transforming AI from a reactive system into a proactive agent capable of independent decision-making and achieving objectives through reasoned execution.

For agentic AI systems to move beyond mere language processing and truly function in the real world, a reliable interface between the Large Language Model and external resources is crucial. This necessitates more than simply prompting an LLM; it demands a framework allowing the model to dynamically select, utilize, and chain together various tools and services – from search engines and APIs to databases and even physical robots. Such a mechanism enables the LLM to not only reason about a goal but also to actively act upon it, breaking down complex tasks into manageable steps, executing them with appropriate tools, and iteratively refining its approach based on the results. This capability transforms the LLM from a passive information provider into an active agent capable of autonomous problem-solving and achieving specified objectives within complex, dynamic environments.

Agentic AI leverages tool intelligence-integrated across perception, reasoning, action, and memory-to enhance its capabilities and achieve complex tasks.
Agentic AI leverages tool intelligence-integrated across perception, reasoning, action, and memory-to enhance its capabilities and achieve complex tasks.

Tool Intelligence: Extending the Reach of Contingency

Tool Intelligence represents an agent’s capacity to move beyond purely computational tasks and engage with physical infrastructures through the utilization of external tools. This functionality establishes a critical link between an agent’s reasoning processes – its ability to analyze data and formulate plans – and its actuation capabilities, allowing it to directly implement those plans in the real world. Rather than simply processing information, a tool-intelligent agent can initiate actions, such as triggering diagnostics, reconfiguring network devices, or collecting telemetry data, by interfacing with appropriate tools and APIs. This extends the agent’s operational scope from the digital realm to encompass and actively manage physical systems.

HPE Marvis exemplifies tool intelligence by autonomously correlating user-reported issues with system telemetry and initiating diagnostic procedures without manual intervention. When a user submits a complaint, Marvis analyzes the reported problem and proactively accesses relevant infrastructure tools – including performance monitoring and logging systems – to identify the root cause. This automated diagnostic process extends beyond simple alerting; Marvis can execute predefined workflows, such as running specific scripts or analyzing log files, to gather further data and validate potential solutions, ultimately reducing resolution times and minimizing manual effort for IT staff.

Effective tool intelligence relies on standardized interfaces for data acquisition and device control. The Prometheus Client provides a mechanism for collecting network telemetry data, exposing metrics via a pull-based system that allows agents to monitor network performance and identify potential issues. Complementing this, the gNMI (gRPC Network Management Interface) API facilitates device reconfiguration and control through a standardized, model-driven approach utilizing gRPC for communication. These tools ensure agents can not only observe system states but also actively modify configurations, enabling automated remediation and proactive maintenance based on gathered telemetry.

The proposed algorithm leverages a heterogeneous toolkit and intelligent UAV motion <span class="katex-eq" data-katex-display="false">\UAV</span> to solve complex scenarios by optimizing tool activation and utilizing a training shield to maximize reward.
The proposed algorithm leverages a heterogeneous toolkit and intelligent UAV motion \UAV to solve complex scenarios by optimizing tool activation and utilizing a training shield to maximize reward.

Engineering for Resilience: The Illusion of Control

Effective Tool Engineering represents a progression beyond simply equipping agents with tools; it emphasizes a systematic approach to maximizing tool utility within agentic systems. This involves three core processes: tool creation, focused on developing tools specifically suited to the agent’s tasks; tool selection, which dynamically chooses the most appropriate tool from a potentially large set based on context and task requirements; and tool learning, enabling agents to refine tool usage through experience and feedback, improving efficiency and effectiveness over time. By concentrating on these areas, Tool Engineering aims to move beyond basic tool use to a state of optimized tool integration, allowing agents to achieve more complex objectives and adapt to changing circumstances.

Zero-Trust Tool Execution enforces strict verification of every tool before granting access or execution privileges, regardless of its origin or network location. This approach mitigates risks associated with compromised or malicious tools in open environments by assuming no implicit trust. Key components include continuous authentication and authorization, granular access control based on the principle of least privilege, and comprehensive logging and monitoring of all tool activity. Implementing Zero-Trust requires validating tool integrity through methods like cryptographic signatures and runtime attestation, ensuring that only authorized and unmodified tools can interact with the system and its data. This methodology is particularly critical in agentic systems where tools operate autonomously and may access sensitive information or control critical functions.

Latent Tool Interfaces improve performance in agentic systems by minimizing communication latency through the use of compressed semantic representations. Traditional tool interactions require full serialization and deserialization of data, incurring significant overhead. Latent interfaces address this by encoding tool inputs and outputs into a lower-dimensional latent space. This compressed representation reduces the data transfer volume and allows for faster processing. The system learns to map between the latent space and the actual tool input/output space, enabling efficient communication and faster task completion without sacrificing functionality. This approach is particularly beneficial in network-constrained environments or when dealing with complex tools requiring substantial data exchange.

The proposed algorithm successfully guides the UAV along complex trajectories, as demonstrated by the tool activations in (b)-(f) compared to the unguided trajectory in (a).
The proposed algorithm successfully guides the UAV along complex trajectories, as demonstrated by the tool activations in (b)-(f) compared to the unguided trajectory in (a).

The Horizon Beckons: Complexity as the Default State

Agentic AI systems are increasingly capable of tackling intricate engineering problems through the integration of Tool Intelligence, exemplified by their ability to perform complex beamforming calculations using tools like the MATLAB Simulator. This extends beyond simple scripting; the AI doesn’t just run the simulator, but actively formulates the parameters, interprets the results, and iteratively refines its approach to optimize signal transmission. Beamforming, crucial for modern wireless communication (e.g., 5G, 6G), involves focusing radio signals to specific receivers, demanding substantial computational power and precise control over antenna arrays. By offloading these calculations to specialized software, agentic AI can efficiently explore a vast design space, adapting to real-time network conditions and optimizing performance metrics such as signal strength, interference reduction, and energy efficiency – a task previously requiring significant human expertise and time.

The increasing accessibility of sophisticated artificial intelligence is driven by the integration of powerful foundational tools via Application Programming Interfaces (APIs). Platforms such as Google’s Gemini and OpenAI’s GPT are no longer isolated systems; instead, they function as orchestrators, leveraging specialized software for complex tasks. This approach democratizes access to capabilities previously confined to expert users, allowing developers to incorporate features like advanced simulations and data analysis into a wider range of applications. By abstracting the intricacies of these tools, these platforms lower the barrier to entry, fostering innovation and enabling a more diverse user base to harness the power of AI without requiring deep technical expertise in the underlying technologies.

The fluctuating demands and interference inherent in modern wireless networks necessitate intelligent, real-time adaptation – a challenge Agentic AI is uniquely positioned to address. Driven by 3GPP standards which govern cellular technologies, these networks constantly evolve, requiring dynamic beamforming and resource allocation. The ability of an AI agent to not only interpret these standards, but to leverage simulation tools for predictive modeling, allows it to proactively adjust network parameters. This means optimizing signal strength, minimizing interference, and ensuring consistent quality of service even as user density shifts and environmental conditions change. Consequently, these adaptive capabilities are not simply about improving performance; they are critical for enabling the next generation of reliable, high-capacity wireless communication and unlocking the full potential of technologies like 5G and beyond.

The proposed algorithm successfully guides the UAV along complex trajectories, as demonstrated by the tool activations in (b)-(f) compared to the unguided trajectory in (a).
The proposed algorithm successfully guides the UAV along complex trajectories, as demonstrated by the tool activations in (b)-(f) compared to the unguided trajectory in (a).

The pursuit of agentic AI, as detailed in this work, resembles a garden rather than a factory. This paper demonstrates how augmenting large language models with external tools-a form of careful cultivation-yields agents capable of sophisticated tasks like UAV trajectory planning. It’s a recognition that intelligence isn’t solely about the model itself, but the ecosystem surrounding it. Paul Erdős observed, “A mathematician knows a lot of formulas but a genius knows a few.” Similarly, these agents don’t need every possible tool, only the right ones, intelligently applied, to navigate the inherent chaos of communication networks. The focus on tool engineering isn’t about control, but about fostering an environment where useful behaviors emerge.

What’s Next?

The pursuit of agentic AI within communication networks reveals, not a destination, but a perpetually receding horizon. This work, while demonstrating the utility of tool integration, merely sketches the topography of a far larger wilderness. The architectures employed are, inevitably, prophecies of future failure – constraints imposed today will become the bottlenecks of tomorrow. There are no best practices – only survivors, those systems which, through accident or design, prove resilient enough to absorb the inevitable shocks.

The current emphasis on reinforcement learning as a means of orchestrating these agents is a temporary reprieve. Such methods excel at navigating known failure modes, yet struggle with the truly novel. A deeper understanding of emergent behavior – of how complex systems self-organize under stress – remains critical. The illusion of control is comforting, but order is just cache between two outages.

Future efforts must shift from constructing ‘intelligent’ agents to cultivating ecosystems where intelligence can arise spontaneously. The focus should not be on dictating behavior, but on establishing the conditions for adaptation. This necessitates a move beyond task-specific tools, toward flexible, composable systems capable of evolving in response to an unpredictable world. The challenge isn’t building AI, it’s growing it.

Original article: https://arxiv.org/pdf/2601.08259.pdf

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

See also:

2026-01-14 11:59