NetMoniAI: Agentic AI for Network Monitoring

• NetMoniAI is an agentic AI framework for network monitoring that integrates decentralized micro-agent analysis with a centralized correlation controller to enhance threat detection.
• Its two-layer architecture deploys autonomous node-level agents for packet-level and flow analysis, providing local anomaly detection and semantic reasoning.
• The framework addresses scalability challenges by balancing detailed visibility with efficient monitoring, which improves response times and reduces processing redundancy.

NetMoniAI is an agentic AI framework for automatic network monitoring and security that integrates decentralized analysis with lightweight centralized coordination (Zambare et al., 12 Aug 2025). In the paper that introduces it, the framework consists of two layers: autonomous micro-agents at each node perform local traffic analysis and anomaly detection, and a central controller then aggregates insights across nodes to detect coordinated attacks and maintain system-wide situational awareness (Zambare et al., 12 Aug 2025). The work is positioned against a recurring systems problem in modern networks: packet-level monitoring provides rich visibility but is expensive to scale, while flow-level monitoring is lighter but can lose accuracy and timeliness; NetMoniAI is proposed as a hybrid alternative for cloud, enterprise, and edge environments (Zambare et al., 12 Aug 2025). A broader survey of generative AI for network monitoring and management places such systems within an emerging shift toward foundation-style models, representation conversion, and network digital assistance (Bovenzi et al., 12 Feb 2025).

1. Definition, scope, and problem setting

NetMoniAI is presented as an agentic AI framework for automatic network monitoring and security whose stated goals are to support scalable monitoring under resource constraints, enable decentralized local detection, provide coordination for cross-node threat identification, improve response time, reduce redundant processing, and maintain accuracy while remaining interpretable to operators (Zambare et al., 12 Aug 2025). The framework is motivated by the claim that many deployed monitoring systems still depend on manual interpretation, static rule engines, and centralized log analysis, which makes them slow to adapt, prone to false positives, and difficult to maintain under evolving threats (Zambare et al., 12 Aug 2025).

The “agentic AI” aspect is central to the authors’ framing. At each node, a lightweight autonomous micro-agent continuously senses, decides, and acts without depending on static detection rules or full central control; these agents capture traffic, detect anomalies, and invoke semantic reasoning using LLMs or a local BERT model (Zambare et al., 12 Aug 2025). A central controller aggregates the structured outputs from distributed agents to infer broader attack intent and coordinated patterns, but the paper emphasizes that the controller does not override the agents and instead acts as an advisory and correlation layer that preserves local autonomy (Zambare et al., 12 Aug 2025). This distinguishes NetMoniAI from purely centralized monitoring, which can become a bottleneck, and from purely decentralized monitoring, which can miss coordinated multi-node threats (Zambare et al., 12 Aug 2025).

Within the wider literature, NetMoniAI belongs to a line of work seeking to make monitoring systems adaptive, semantically aware, and operationally useful rather than merely threshold-driven. Earlier work on passive monitoring argued for adaptive alarming and diagnosis assistants based on expert rules, fuzzy logic, and neural-network adaptation (Ahmad et al., 2013), while recent LLM-based monitoring systems have emphasized reasoning over heterogeneous telemetry stores and natural-language operator interaction (Yoon et al., 30 Jul 2025). This suggests that NetMoniAI should be understood not as a replacement for telemetry collection, but as a framework that reorganizes monitoring into distributed sensing, local reasoning, and centralized correlation.

2. Two-layer architecture

NetMoniAI uses a two-layer design: the lower layer consists of autonomous node-level AI agents deployed on individual machines or network nodes, and the upper layer is a centralized controller AI agent that performs higher-level reasoning and multi-agent integration (Zambare et al., 12 Aug 2025). In textual form, the system diagram can be read as follows: traffic and system conditions are observed locally at each node; local agents perform packet capture, analysis, anomaly detection, semantic reasoning, and report generation; those reports are transmitted upward to the central controller; the controller correlates reports across time and across nodes, extracts shared intent, and exposes aggregated network-wide insights through a dashboard and chatbot; recommendations or alerts are then made available back to the system, while local agents retain execution autonomy (Zambare et al., 12 Aug 2025).

Layer	Components	Function
Node layer	autonomous micro-agents	local traffic analysis and anomaly detection
Controller layer	centralized controller AI agent	multi-agent integration, correlation, intent extraction
Operator interface	dashboard and chatbot	network-wide insights, alerts, recommendations

This architecture is explicitly hybrid. Local traffic analysis includes both packet-level and flow-level perspectives, although the actual raw processing appears packet-centric at the node and correlation-centric at the controller (Zambare et al., 12 Aug 2025). The design therefore combines localized observation with network-wide synthesis. A plausible implication is that the framework attempts to preserve the responsiveness of edge-local analysis while avoiding the fragmentation of strictly local detectors.

The architectural choice also aligns with other work on scalable monitoring under telemetry and systems constraints. SDN monitoring research such as CeMon treated telemetry collection itself as a network-wide optimization problem, separating monitoring application demands from low-level polling schemes (Su et al., 2017), while programmable monitoring systems such as StreaMon emphasized the separation between high-level logic and low-level probe primitives (Bianchi et al., 2013). NetMoniAI adopts a different control abstraction, but it shares the same general concern: intelligence becomes useful only if monitoring is organized so that sensing, feature extraction, and coordination remain tractable.

3. Node-level micro-agents

Each node-level micro-agent is organized into four layers (Zambare et al., 12 Aug 2025). The Service Layer handles data acquisition and control and includes the Packet Capture Module, Analyzer Engine, Report Generator, Tuning Controller, and Decision Trigger (Zambare et al., 12 Aug 2025). The Agent Layer handles local decision making via Input Handler, Planner, Tool Executor, Short-Term Memory, Execution Loop, and Communication Bus (Zambare et al., 12 Aug 2025). The Model Layer contains the LLM Inference Engine and Function Call Interface, supporting semantic reasoning over traffic features, and the Application Layer provides a Realtime Dashboard and a Conversational Interface (Zambare et al., 12 Aug 2025). The paper also notes that each node agent maintains a lightweight memory buffer for temporally consistent decisions across cycles (Zambare et al., 12 Aug 2025).

Functionally, packet capture and telemetry collection happen locally; thresholding and anomaly triggering happen locally; packet or feature processing and local semantic classification happen locally; structured outputs and summaries are produced locally; and only condensed findings are sent to the central controller (Zambare et al., 12 Aug 2025). The paper describes the Planner in the Agent Layer as choosing next steps based on “rules or LLM-guided logic,” but it does not formalize that policy (Zambare et al., 12 Aug 2025). It likewise does not provide an explicit classifier architecture, threshold equation, anomaly score function, loss function, or decision boundary (Zambare et al., 12 Aug 2025).

The local models are heterogeneous by design. At node level, the inference model $M_i$ is either BERT or an external LLM, and it maps extracted features to a local report and summary; this formulation is described as a reconstruction from prose rather than an equation supplied by the paper (Zambare et al., 12 Aug 2025). The use of local BERT versus remote LLMs places NetMoniAI in the design space identified by the survey literature on GenAI for network monitoring and management, which distinguishes monolithic training, pre-training plus fine-tuning, and fine-tuning-only adaptation, and emphasizes representation conversion from traffic artifacts into model-consumable inputs (Bovenzi et al., 12 Feb 2025).

NetMoniAI’s node design also has a clear affinity with endpoint and edge-side monitoring systems. Research on on-device passive monitoring for smartphones showed that local packet interception, local analysis, and selective upload can support privacy, performance, and classification tasks without requiring continuous remote tunneling (Shuba et al., 2016). NetMoniAI is not a mobile-only framework, but the analogy is instructive: in both cases, local processing reduces the amount of raw telemetry that must be centralized.

4. Central controller and coordinated reasoning

The central controller is itself described as an agentic component with a continuous loop spanning planning, action, memory integration, and tool or function invocation (Zambare et al., 12 Aug 2025). Its core modules are Policy Generator, Subtask Breakdown, Execution Monitor, Intent Extractor, Communication Module, and Memory Store (Zambare et al., 12 Aug 2025). The controller groups incoming reports by source, timestamp, and threat type; it performs correlation, intent extraction, and system-wide threat analysis; and it uses an Aggregation Module implemented with pandas for in-memory clustering of incoming reports (Zambare et al., 12 Aug 2025). The Intent Extractor compares semantic summaries across agents to infer shared attacker behavior, and a short-term memory buffer stores recent events to enable temporal correlation, especially for distributed or stealthy threats (Zambare et al., 12 Aug 2025).

The controller’s outputs are exposed through a WebSocket dashboard and chatbot, and the paper states that the controller suggests mitigations or observation adjustments rather than directly commanding nodes (Zambare et al., 12 Aug 2025). This point is central to