Cross-Domain Diagnostics with AI and Agentic Systems

Introduction

Imagine you are an IT operations engineer and someone hands you a log file -- perhaps from a Kubernetes flannel pod -- and simply asks, "What is the issue?" You open the file in your favorite editor. You run a couple of grep commands using terms that you know commonly hint at problems. You scroll through thousands of lines, hoping your trained eye catches something unusual. Now multiply that scenario across an entire data center fabric with spine-leaf switches, routers, BFD sessions, BGP peers, ISIS adjacencies, and hundreds of streaming telemetry counters. The complexity becomes overwhelming. This is exactly where cross-domain AI enters the picture, offering a fundamentally new approach to network diagnostics that spans multiple layers, multiple sources, and multiple domains simultaneously.

Industry projections underscore the urgency. By 2025, an estimated 40 percent of services engagements will include GenAI-enabled delivery, triggering a major shift in how organizations approach strategy, change management, and training. By 2028, GenAI technology is projected to handle 35 percent of network configuration and troubleshooting activities -- up from near zero in 2023. The potential market is staggering: rather than hundreds of billions, the opportunity to replace services with software could reach tens of trillions.

This article explores the full pipeline of agentic diagnostics and AI network diagnostics -- from filtering massive log files so they fit within an LLM context window, to analyzing high-dimensional streaming telemetry with unsupervised methods, to diagnosing cross-domain, multi-layer events that no single tool or human operator could catch alone. Whether you are preparing for a certification exam or managing a production network, understanding these techniques will become essential as AI-driven operations move from experimental to mainstream.

What Is Cross-Domain AI for Network Diagnostics?

Cross-domain AI refers to the application of artificial intelligence techniques that operate across multiple network domains, layers, and data sources simultaneously. Traditional troubleshooting tends to be siloed: a network engineer examines router logs, a systems administrator reviews server metrics, and a security analyst investigates firewall events -- each working independently. Cross-domain AI breaks down these silos by ingesting and correlating data from all of these sources at once.

The core question driving this approach is straightforward: Can we leverage the wealth of information available in a device -- logs, streaming telemetry, interface counters, protocol state -- to have it tell us in natural language what its state is?

This vision encompasses three major diagnostic layers:

Diagnosing logs with the help of AI -- Using language models to interpret and summarize log files that are too large and complex for manual review.
Diagnosing streaming network telemetry with the help of AI -- Applying statistical and machine learning methods to high-dimensional, time-series telemetry data to detect anomalies and change points.
Diagnosing cross-domain, multi-layer, multi-source telemetry with the help of AI -- Correlating events across different device types, protocol domains, and data formats to identify root causes that span the entire infrastructure.

Each of these layers builds on the previous one, creating a comprehensive diagnostic pipeline that moves from raw data to actionable insight.

How Does AI Diagnose Network Logs?

Log files are the first-line diagnostic resource for most network and systems engineers. They capture everything from routine keepalive messages to critical error conditions. But log files present a fundamental challenge for AI-assisted analysis: they are often too large to process directly.

Consider a real-world example. A Kubernetes flannel daemon set pod produces a log file called kube-flannel-ds-xd5wp.log. Running a simple word count reveals the scale of the problem:

$ wc kube-flannel-ds-xd5wp.log
4490 89657 687954 kube-flannel-ds-xd5wp.log

This single log file contains 4,490 lines, 89,657 words, and 687,954 characters. That volume of text is too large to fit into the context window of many large language models. You cannot simply paste the entire file into a prompt and ask "what is wrong?" -- the model will either truncate the input or refuse to process it.

This means we need a method to filter and reduce the log file before sending it to an LLM, without losing the key information that reveals the actual problem. The goal is different from traditional log anomaly detection. We are not trying to classify every line as normal or anomalous. Instead, we want to shrink the dataset to a level where it fits into a context window while preserving the lines that matter most.

Pro Tip: The distinction between log reduction and log anomaly detection is critical. Log reduction focuses on making the data consumable by an LLM. Anomaly detection focuses on identifying specific problematic patterns. Both can use similar techniques, but they serve different purposes in the diagnostic pipeline.

What Is TF-IDF and How Does It Filter Log Files for Cross-Domain AI?

The technique used to reduce log files for LLM consumption draws on a well-established statistical method called TF-IDF (Term Frequency -- Inverse Document Frequency). TF-IDF allows us to score the importance of words in a document based on how frequently they appear across multiple documents.

The Core Intuition

The fundamental idea behind TF-IDF for log analysis can be summarized as: "Watch out for an influx of rare things." A term that is rare across the entire log file but appears frequently in a specific section or line is more important than a term that appears everywhere.

TF-IDF is the product of two statistics:

Component	Definition	Effect on Score
Term Frequency (TF)	How often a word appears in a specific document (line)	High frequency in a line = higher score
Inverse Document Frequency (IDF)	How many documents (lines) contain the word	Appears in many lines = lower score

If a word appears frequently in a specific log line, it receives a high TF score.
If that same word appears across many log lines (meaning it is common), it receives a low IDF score, which reduces its overall importance.
The combination ensures that rarely occurring but informative lines rise to the top, while repetitive "everything is working" messages sink to the bottom.

Applying TF-IDF to Log Files

When applied to log diagnostics, TF-IDF treats the data as follows:

Each line in the log file represents a "document" in TF-IDF terminology.
The entire log file represents the corpus (the collection of all documents).
Lines are then sorted in decreasing order of their maximum per-word TF-IDF weight -- that is, sorted by rarity.

The result is a transformed version of the original log file where:

Rarely occurring informative lines appear at the top. These are the lines most likely to contain error messages, unusual state transitions, or one-time events that point to the root cause.
Repetitive "everything works" lines appear at the bottom. These are the routine keepalive confirmations, periodic status updates, and other noise that dominates most log files.

Improving TF-IDF Results

Several techniques can enhance the quality of TF-IDF-based log reduction:

Stemming -- Reducing words to their root form so that variations like "connecting," "connected," and "connection" all count as the same term. This prevents the algorithm from treating these as separate rare words when they are all part of normal operation.
Different tokenization approaches -- Adjusting how log lines are split into individual terms. For example, treating IP addresses as single tokens rather than splitting them at each dot, or preserving error codes as complete units.

After applying TF-IDF sorting and taking the top portion of the reranked log file, the reduced dataset fits comfortably within an LLM's context window. The language model can then analyze these filtered lines and provide a natural-language diagnosis of the issue.

Pro Tip: When implementing TF-IDF for log reduction, experiment with the cutoff threshold. Taking the top 10-20 percent of reranked lines is often sufficient to capture the meaningful events while staying within context window limits. The exact percentage depends on the log file's characteristics and the LLM's token capacity.

Why Can LLMs Understand and Diagnose IT System Logs?

A natural question arises: why are large language models effective at diagnosing IT system logs in the first place? The answer lies in their training data and architecture.

Modern LLMs have been trained on vast corpora that include technical documentation, stack traces, error message databases, troubleshooting guides, and community forums. As a result, they have internalized patterns that associate specific log messages with known issues, failure modes, and remediation steps.

When presented with filtered log data -- the rare and informative lines surfaced by TF-IDF -- an LLM can:

Recognize error patterns that match known failure conditions.
Correlate multiple log entries to identify a sequence of events leading to a failure.
Explain the issue in natural language, translating cryptic log messages into clear descriptions that operators can act on.
Suggest potential remediation steps based on the patterns it has learned from its training data.

The key enabling factor is the preprocessing step. Without TF-IDF or a similar reduction method, the LLM would be overwhelmed by noise. With it, the LLM receives a concentrated signal that maximizes its diagnostic capability.

This combination of statistical filtering and language model interpretation represents the foundation of AI-assisted log diagnostics. It transforms the traditional manual process -- browsing, grepping, reading line by line -- into an automated pipeline that delivers results in seconds rather than hours.

How Does Streaming Telemetry Feed Cross-Domain AI Diagnostics?

Moving beyond log files, the next frontier for cross-domain AI is streaming network telemetry. While logs capture discrete events, streaming telemetry provides continuous, real-time measurements of network state -- interface counters, CPU utilization, memory usage, protocol session status, and much more.

The Telemetry Data Challenge

Consider a realistic network topology used for diagnostic testing: a spine-leaf fabric with multiple spine switches (spine1 through spine6), leaf switches (leaf2 through leaf8), distribution routers (DR01, DR02, DR03), and rack switches (rswA2 through rswB3). Traffic generators push synthetic traffic through the fabric while controlled events -- interface shutdowns, BFD session breaks -- are injected at specific times.

A typical test scenario involves a sequence of synthetic events with precise timestamps:

Relative Time	Event	Device	Interface
0.000s	IxChariot traffic starts	--	--
6.168s	Break BFD	spine4-3464	eth1/25
1200.019s	Shutdown interface	leaf7	HundredGigE0/0/0/10
2406.067s	Enable BFD	spine4-3464	eth1/25
3599.987s	Enable interface	leaf7	HundredGigE0/0/0/10
4806.079s	Break BFD	spine4-3464	eth1/25
5999.987s	Shutdown interface	leaf7	HundredGigE0/0/0/10
7206.100s	Enable BFD	spine4-3464	eth1/25
8400.084s	Enable interface	leaf7	HundredGigE0/0/0/10
9606.081s	Break BFD	spine4-3464	eth1/25
10799.984s	Shutdown interface	leaf7	HundredGigE0/0/0/10
10807.827s	IxChariot traffic stopped	--	--
10913.341s	Collect telemetry	--	--

These events cycle through BFD session disruptions and interface shutdowns/restorations at roughly 20-minute intervals, generating ripple effects across the entire fabric. The challenge is to detect and diagnose these events solely from the telemetry data, without prior knowledge of what was injected.

The Scale of Telemetry Data

The telemetry collected from even a single device is enormous. For "Leaf7" alone, the dataset dimensions are 1,079 rows by 7,334 columns. Each column represents a different telemetry counter or state variable, and each row represents a point in time.

The telemetry sources span a wide range of IOS-XR operational models:

Memory: Cisco-IOS-XR-nto-misc-oper:memory-summary/nodes/node/summary
CPU: Cisco-IOS-XR-wdsysmon-fd-oper:system-monitoring/cpu-utilization
IPv4/IPv6 traffic: Cisco-IOS-XR-ipv4-io-oper and Cisco-IOS-XR-ipv6-io-oper
TCP state: Cisco-IOS-XR-ip-tcp-oper:tcp, tcp-connection, tcp-nsr
BFD sessions: Cisco-IOS-XR-ip-bfd-oper:bfd/session-briefs, bfd/counters, bfd/summary
ISIS adjacencies: Cisco-IOS-XR-clns-isis-oper:isis/instances/instance/neighbors/neighbor
ISIS interfaces: Cisco-IOS-XR-clns-isis-oper:isis/instances/instance/interfaces/interface
ISIS routes: Cisco-IOS-XR-clns-isis-oper:isis/instances/instance/topologies/topology/ipv4-routes/ipv4-route
BGP state: Cisco-IOS-XR-ipv4-bgp-oper:bgp/instances/instance/instance-active/default-vrf/afs/af/af-process-info/global
BGP process info: Cisco-IOS-XR-ipv4-bgp-oper:bgp/instances/instance/instance-active/default-vrf/process-info
Interface statistics: Cisco-IOS-XR-infra-statsd-oper:infra-statistics/interfaces/interface[interface-name=Hu*]
Interface briefs: Cisco-IOS-XR-pfi-im-cmd-oper:interfaces/interface-briefs/interface-brief
Interface details: Cisco-IOS-XR-pfi-im-cmd-oper:interfaces/interface-xr/interface
FIB statistics: Cisco-IOS-XR-fib-common-oper:fib-statistics
Ethernet statistics: Cisco-IOS-XR-drivers-media-eth-oper:ethernet-interface/statistics/statistic
IPv4/IPv6 ARM: Cisco-IOS-XR-ip-iarm-v4-oper and Cisco-IOS-XR-ip-iarm-v6-oper

Each of these YANG model paths produces multiple counters, resulting in the 7,334 feature columns for a single leaf switch. Across the entire fabric, the data volume grows by an order of magnitude.

What Are the Characteristics of Network Telemetry Data for AI Analysis?

Before applying AI methods to streaming telemetry, it is essential to understand the nature of the data. Network telemetry data has several distinctive characteristics that influence how it must be processed:

Time-Series Nature

All telemetry data is inherently time-series: each measurement is associated with a timestamp, and the sequence of values over time reveals trends, patterns, and anomalies. The timestamps in the test scenario use nanosecond-precision absolute timestamps (e.g., 1.558.249.381.658.610), which must be converted to relative timestamps for analysis.

High Dimensionality

With 7,334 columns for a single device, the feature space is enormous. Many of these features are correlated (e.g., bytes received on one interface may correlate with bytes transmitted on a connected interface), which creates opportunities for dimensionality reduction. Research on the sensitivity of PCA (Principal Component Analysis) for traffic anomaly detection has demonstrated that network data often has much lower intrinsic dimensionality than the raw feature count suggests.

Variable Feature Availability

Not all features are available at all times or across all devices. Some counters may only exist when a particular protocol is active. The number of available features varies:

Over time -- Features may appear or disappear as protocols come up or go down.
Over different devices -- Different device roles (spine vs. leaf vs. router) expose different telemetry counters.

Heterogeneous Data Types

The features differ fundamentally in their nature:

Characteristic	Examples	Impact
Units	Bytes, packets, sessions, percentage, count	Cannot be compared directly
Behavior	Incremental counters vs. gauge values	Require different preprocessing
Interpretation	A rising byte counter is normal; a rising error counter is not	Context-dependent analysis needed

This heterogeneity means that raw telemetry data cannot simply be fed into a machine learning algorithm. Significant preprocessing is required to make the data suitable for analysis.

How Is Telemetry Data Preprocessed for Cross-Domain AI?

Data preprocessing is the critical bridge between raw telemetry collection and AI-driven diagnostics. The preprocessing pipeline consists of several stages, each designed to address a specific challenge of network telemetry data.

Step 1: Consolidate Various Data Sources

The first step is to align data from multiple sources into a unified time-series structure. Telemetry from different devices and different YANG model paths arrives at different intervals and with different timestamps. Consolidation involves:

Aligning all data sources to a common time axis.
Handling missing values where a particular counter was not reported at a given time.
Merging data from multiple devices into a single analytical framework.

The original timestamps in the dataset show varying intervals between consecutive samples. For example, intervals between successive data points might be 10.016 seconds, 10.017 seconds, 10.107 seconds, 13.344 seconds, or 12.668 seconds. After consolidation and resampling, these are normalized to a consistent 10.0-second interval, creating a regular time grid for analysis.

Step 2: Make Heterogeneous Time Series Comparable

Raw telemetry values exist on vastly different scales. Interface bytes received might be in the billions, while BFD session counts might be in the single digits. Comparing these directly would cause the high-magnitude features to dominate any analysis.

Scaling methods address this problem:

Min-Max Scaling -- Transforms each feature to a fixed range (typically 0 to 1) based on its minimum and maximum observed values.
Z-Normalization (Standardization) -- Transforms each feature to have zero mean and unit standard deviation.

By scaling the data, every univariate time series evolves within comparable ranges. This is necessary for downstream processing methods that operate on the combined feature space.

Step 3: Smooth the Data

Exponential moving average smoothing is applied to focus on the baseline behavior of each time series and remove background variance. High-frequency noise in telemetry data -- jitter in packet counts, minor fluctuations in CPU utilization -- is not informative for detecting major change events. Smoothing filters out this noise while preserving the significant transitions that indicate real network events.

Step 4: Differentiate Incremental Time Series

Network telemetry includes both incremental counters (like total bytes transmitted, which only increase) and gauge values (like CPU utilization, which fluctuates). To compare these fundamentally different data types:

Differentiating the incremental time series converts them from cumulative values to rate-of-change values.
This transforms a monotonically increasing counter into a series of deltas that can be meaningfully compared with gauge values.
Combined with scaling, differentiation enables the comparison of data of different types and ranges within a single analytical framework.

Pro Tip: The order of preprocessing steps matters. A recommended sequence is: (1) consolidate and align timestamps, (2) differentiate incremental counters, (3) apply scaling, and (4) smooth. Changing this order can produce artifacts that mask real events or create false positives.

After the full preprocessing pipeline, the data for Leaf7 -- originally 1,079 rows by 7,334 raw columns -- is transformed into a clean, comparable, regularly-sampled dataset ready for change-point detection.

How Are Change Points Detected in Cross-Domain AI Diagnostics?

With preprocessed telemetry data in hand, the next step is to detect change points -- moments in time when something significant occurs in the network. The diagnostic approach uses unsupervised methods, meaning it does not require labeled training data or prior knowledge of what events occurred.

The fundamental question is: Can we discover and diagnose the synthetic events solely by analyzing the data using an unsupervised method?

The change-point detection pipeline consists of three phases:

Data preprocessing (covered in the previous section)
Detect change points -- Identify the specific timestamps where significant shifts occur in the telemetry data.
Diagnose a change point -- Once a change point is identified, determine which features (counters, protocol states) contributed most to the change, thereby revealing the root cause.

Dimensionality Reduction

Given the high dimensionality of the data (thousands of features), directly searching for change points in the raw feature space is computationally expensive and prone to noise. Research on PCA for traffic anomaly detection has shown that network data can often be effectively represented in a much lower-dimensional space.

Dimensionality reduction techniques like PCA project the high-dimensional data onto a smaller set of principal components that capture the most variance in the data. Change-point detection is then performed in this reduced space, where real events produce clear signals while noise is suppressed.

From Detection to Diagnosis

Detecting that a change occurred at a particular timestamp is only half the challenge. The critical value of cross-domain AI comes from diagnosing what changed and why. By examining which original features (out of the 7,334) contributed most to the detected change point, the system can identify:

Which interfaces experienced traffic shifts.
Which protocol sessions (BFD, BGP, ISIS) changed state.
Whether the event was localized to a single device or propagated across the fabric.
The temporal sequence of cascading effects across domains.

This diagnostic capability transforms raw telemetry from an overwhelming data firehose into an actionable narrative: "At time T, BFD session on spine4-3464 eth1/25 went down, causing ISIS adjacency changes on leaf7, which triggered BGP route withdrawals and a traffic shift to alternate paths."

Agentic Diagnostics: The Future of AI-Driven Network Operations

The combination of log analysis and telemetry diagnostics described above represents the current state of the art. But the trajectory points toward something more ambitious: agentic systems that can autonomously investigate, correlate, and resolve network issues across domains.

An agentic diagnostic system goes beyond a single prompt-response interaction with an LLM. Instead, it operates as an autonomous agent that can:

Gather data proactively -- Querying devices for additional telemetry when initial analysis suggests a particular hypothesis.
Correlate across domains -- Automatically linking a BFD session failure on a spine switch to a traffic pattern change observed in application-layer metrics.
Iterate on hypotheses -- If the first analysis is inconclusive, the agent refines its approach, requests additional data, and tests alternative explanations.
Communicate in natural language -- Presenting findings in clear, actionable language rather than raw data dumps.

The ability to interact in a humanlike capability represents one of the biggest opportunities for AI in network operations. Rather than replacing engineers, agentic systems augment their capabilities by handling the tedious data gathering and correlation work, allowing engineers to focus on decision-making and strategic planning.

Cross-Domain Correlation in Practice

Consider how agentic diagnostics would handle the spine-leaf fabric scenario described earlier. When an interface goes down on leaf7 (HundredGigE0/0/0/10), the effects cascade across multiple domains:

Layer 1/2: Interface counters drop to zero, link state changes.
BFD: Associated BFD sessions detect the failure within milliseconds.
ISIS: Adjacencies are torn down, SPF recalculations triggered, routes withdrawn and redistributed.
BGP: Route withdrawals propagate, path selection shifts to alternate routes.
Traffic: Data plane traffic reconverges onto surviving paths, potentially causing congestion.
Resource utilization: CPU spikes on affected devices during reconvergence, memory allocation increases for new route entries.

A traditional monitoring system would generate separate alerts for each of these effects, potentially overwhelming the operations team with dozens of notifications for a single root cause. An agentic diagnostic system correlates all of these signals, identifies the root cause (interface shutdown on leaf7), and presents a unified analysis.

Statistical vs. Embedding-Based Approaches for AI Network Diagnostics

The reference material highlights two broad categories of methods for log anomaly detection and reduction, each with distinct advantages:

Statistical Approaches

Statistical methods use mathematical techniques to identify patterns without requiring neural network training:

Method	Description	Strengths
TF-IDF	Scores term importance by frequency and rarity	Simple, interpretable, fast
Token frequency analysis	Counts word occurrences across log segments	Easy to implement, no training needed
Group similar log lines	Clusters logs using TF-IDF similarity	Reduces redundancy effectively

These approaches are particularly well-suited for log reduction because they are fast, do not require labeled data, and produce interpretable results. The TF-IDF method described earlier is a prime example: it requires no training, runs in seconds, and produces a clearly ranked output.

Embedding-Based Approaches

Embedding-based methods use neural networks to create dense vector representations of log entries:

Method	Description	Strengths
LogBERT	BERT-based model trained on log data	Captures semantic meaning
LogBD	Log-specific embedding model	Domain-optimized representations
LSADNET	Anomaly detection network for logs	End-to-end anomaly scoring

These methods can capture deeper semantic relationships between log entries but require more computational resources and potentially training data. They excel at identifying subtle anomalies that statistical methods might miss.

Pro Tip: In practice, a hybrid approach often works best. Use statistical methods like TF-IDF for initial log reduction (fast and reliable), then apply embedding-based methods for deeper analysis of the reduced dataset. This balances speed with analytical depth.

Building a Complete Cross-Domain AI Diagnostic Pipeline

Bringing together all the techniques discussed, a complete cross-domain AI diagnostic pipeline consists of the following stages:

Stage 1: Data Ingestion

Collect log files from network devices, servers, and applications.
Stream telemetry via model-driven telemetry (YANG models, gRPC/gNMI subscriptions).
Gather state information from protocol databases (BFD, BGP, ISIS, interface state).

Stage 2: Data Reduction and Preprocessing

Apply TF-IDF to reduce log files to their most informative lines.
Consolidate telemetry from multiple sources onto a common time axis.
Scale, smooth, and differentiate time-series data to enable cross-feature comparison.

Stage 3: Anomaly and Change-Point Detection

Use dimensionality reduction (PCA) to project high-dimensional telemetry into a manageable space.
Apply unsupervised change-point detection algorithms to identify significant events.
Score the severity and confidence of each detected event.

Stage 4: Diagnosis and Correlation

For each detected event, identify contributing features to determine root cause.
Correlate events across devices and domains to build a causal chain.
Feed filtered log data and event summaries to an LLM for natural-language diagnosis.

Stage 5: Reporting and Action

Present findings in natural language with supporting data visualizations.
Recommend remediation steps based on diagnosed root cause.
Log the diagnostic process for future reference and model improvement.

This pipeline represents the trajectory that network operations is heading toward -- from reactive, manual troubleshooting to proactive, automated, cross-domain diagnostics powered by AI.

Frequently Asked Questions

What is cross-domain AI in the context of network diagnostics?

Cross-domain AI refers to artificial intelligence techniques that analyze data from multiple network domains -- routing, switching, security, application performance -- simultaneously. Instead of troubleshooting each domain in isolation, cross-domain AI correlates signals across all layers to identify root causes that span the entire infrastructure. This is particularly valuable in modern networks where a single failure can cascade through BFD, ISIS, BGP, and traffic forwarding domains within seconds.

Why can't I just feed an entire log file into an LLM for diagnosis?

Log files are typically too large for an LLM's context window. A single Kubernetes pod log can contain 4,490 lines with 687,954 characters -- far exceeding the token limits of most models. The solution is to use statistical methods like TF-IDF to reduce the log file by ranking lines by rarity and importance. This surfaces the most diagnostically relevant lines while discarding repetitive "everything is normal" messages, allowing the reduced dataset to fit within the LLM's context window.

What is TF-IDF and why is it useful for log analysis?

TF-IDF stands for Term Frequency -- Inverse Document Frequency. It scores the importance of words based on two factors: how often a word appears in a specific line (Term Frequency) and how rare that word is across the entire log file (Inverse Document Frequency). For log analysis, each log line is treated as a "document" and the entire file as the corpus. Lines are sorted by their maximum per-word TF-IDF weight, pushing rare and informative lines to the top and common repetitive lines to the bottom. Additional techniques like stemming can further improve accuracy.

How does streaming telemetry differ from traditional SNMP polling for AI diagnostics?

Streaming telemetry provides continuous, model-driven data feeds using YANG models and protocols like gRPC/gNMI. Unlike SNMP polling, which samples at fixed intervals and may miss transient events, streaming telemetry captures data at high frequency and pushes it to collectors in near-real time. This granularity is essential for AI diagnostics -- the test scenario described in this article uses nanosecond-precision timestamps to track events that occur within seconds of each other. The telemetry sources span dozens of IOS-XR operational models covering memory, CPU, BFD, ISIS, BGP, interface statistics, FIB, and more.

What preprocessing is needed before AI can analyze network telemetry?

Network telemetry requires extensive preprocessing before AI analysis. The key steps are: (1) consolidating data from multiple sources onto a common time axis with regular intervals, (2) differentiating incremental counters to convert cumulative values into rates of change, (3) scaling all features to comparable ranges using Min-Max or Z-Normalization, and (4) smoothing with exponential moving averages to remove background noise. Without these steps, the heterogeneity of network data -- different units, different behaviors, different scales -- would prevent meaningful cross-feature analysis.

What is an agentic diagnostic system?

An agentic diagnostic system is an AI-powered tool that can autonomously investigate network issues across multiple domains. Unlike a simple chatbot that responds to a single prompt, an agentic system can gather additional data, test hypotheses, correlate events across devices and protocols, and iterate on its analysis until it reaches a conclusion. These systems represent the next evolution of AI-driven network operations, combining the analytical power of LLMs with the ability to interact with network infrastructure directly.

Conclusion

Cross-domain diagnostics with AI and agentic systems represents a fundamental shift in how network operations teams approach troubleshooting and performance management. The techniques covered in this article -- from TF-IDF-based log reduction that makes massive log files consumable by LLMs, to the full telemetry preprocessing pipeline of consolidation, scaling, smoothing, and differentiation, to unsupervised change-point detection in high-dimensional data -- form a comprehensive toolkit for next-generation network diagnostics.

The key takeaways are clear:

Log files can be made AI-ready using statistical methods like TF-IDF that surface rare, informative lines while filtering out noise.
Streaming telemetry provides the raw material for cross-domain analysis, but requires significant preprocessing to handle its heterogeneous, high-dimensional nature.
Unsupervised methods can detect and diagnose events without prior knowledge of what occurred, making them suitable for production environments where the failure modes are unknown.
Agentic systems represent the future, combining autonomous data gathering, cross-domain correlation, and natural-language reporting into a unified diagnostic capability.

As projected industry trends indicate, GenAI-enabled delivery will become integral to services engagements by 2025, and by 2028 these technologies will handle over a third of network configuration and troubleshooting tasks. Engineers who understand the principles behind cross-domain AI diagnostics -- the statistical foundations, the preprocessing requirements, the correlation techniques -- will be best positioned to leverage these tools effectively.

The transition from manual, siloed troubleshooting to AI-driven, cross-domain diagnostics is not a distant future scenario. It is happening now. Explore the courses available on NHPREP to build the skills you need to stay ahead of this transformation, from AI and machine learning fundamentals to hands-on network automation and telemetry configuration.