December 1, 2023

How Automation Turns PCAP Analysis From a Tedious Drain on Resources to a Streamlined Operation

Winston Churchill is infamously attributed with saying “Democracy is the worst form of government — except for all the others.” It’s tempting to suggest something similar about Packet Captures (PCAP) as a tool for network troubleshooting.

There's no doubt that PCAPs are a vital aspect of network analysis. It was only a few weeks ago that we published a blog post emphasizing the importance of Packet Captures. We said it then, and we’ll say it again: PCAPs represent the gold standard for network testing and interconnectivity analysis.

The Limitations of Packet Captures

However, as any network engineer will attest, utilizing packet captures to troubleshoot issues has its limitations.

It’s a resource-intensive and tedious process full of redundant efforts. Going packet by packet to analyze the source of a problem eats up the time, energy and effort of top talent. And with MTTR (Mean Time To Resolution) being a top strategic KPI constantly under consideration for all enterprise applications, saying time is of the essence is a massive understatement.

A table showing the pros and cons of PCAP Analysis
The pros and cons of PCAP Analysis

Automated Network Troubleshooting

But what if there was a way to secure all of the positives without the negatives; to unlock the insight provided by PCAPs without the time and resources associated with manual analysis?

Imagine a more efficient process in which automation, machine learning and interactive visualization are used in collaboration with the gold standard of network analysis.

Such a transformative approach would not only optimize network troubleshooting, it would have the potential to make the entire telecommunications industry more efficient and reliable at a time when modern networks are asked to handle the ever-increasing demand for better network performance and reliability, especially in enterprise applications, where 5G has transformative potential.

The Dynamic Nature of Modern Networks

This type of automated software integration into network processes is both long overdue and urgently needed. The rapid advancements, from the introduction of the first computer networks to the rollout of 5G, have consistently outpaced monitoring tools, often leaving the operations associated with network analysis feeling antiquated.

Today's networks, marked by their intricate designs, advanced functionalities, and multiple connections, are a dramatic departure from the networks of the past when the traditional methods of analysis and performance metrics were first established.

A diagram of a modern network
Modern networks have surpassed the tools used to troubleshoot them

This transformation is underscored by several significant shifts:

Virtualization of Networks

  • The static, predictable nature of old networks is fading. Today, thanks to virtualization, network functions are more fluid, adaptable, and ever-changing.

Cloud Computing

  • The move toward cloud computing enables a ton of benefits like scalable network storage and resources, but it also introduces challenges in data security, latency, and management.

Software-Defined Networking (SDN)

  • Through SDN, network administrators get centralized control and programmable network behavior, but implementing it demands new skills for configuration and security nuances.

Internet of Things Technologies

  • People no longer have a single device connected to the internet. Users have multiple devices — just in a single room of their home. This type of device overload leads to far greater network traffic, necessitating advanced management and security strategies.

Fluctuating Notion of ‘Valid IP Address’

  • Fixed IPs are quickly becoming a thing of the past as IPs are now in constant flux, being reassigned, sometimes even duplicated, adding layers of complexity to network management.

Because of these changes, modern networks move faster than ever in real-time. And yet, the tools used to monitor the status of networks haven't evolved at the same pace.

All the while, the importance of detecting topology shifts and discovering IPs during failures is increasing — especially when the reputation of a telecommunications company depends on providing reliable service.

Evolving Network Troubleshooting Needs

The dynamic elements introduced by the innovations listed above not only require greater network flexibility, but they also present new challenges and new performance issues that require innovative solutions and tools.

Traditional troubleshooting methods and monitoring tools were never designed to cope with today’s scale of tasks like:

  • The complexity of analyzing multiple packets and sequences of messages.
  • The diverse range of protocols governing these messages.
  • The sheer number of network functions and technologies to correlate.

As modern networks continue to evolve and network traffic from users only increases, these issues don’t go away. They compound, leading to an increasingly pressing need for innovative tools — like automation, artificial intelligence, and machine learning — that address modern network complexities and make it easier, not harder, to troubleshoot.

The Tedious Nature of Manual Packet Analysis

We’ve already mentioned the tedious process of manual packet analysis multiple times in this blog post, but what exactly is that process — and what is it that makes it so tedious?

Essentially, this type of analysis is about examining network data PCAPs in detail to understand their structure, content, and behavior within a network. As a way to monitor and troubleshoot issues, it's effective but time-consuming. Instead of automated tools, this level of analysis and testing relies on the expertise, judgment and ability of an analyst.

While their ability is invaluable, several inherent challenges make this manual approach to gather information both cumbersome and time-consuming:

Multiple Packets and Messages

  • Each packet is a puzzle piece. And with countless pieces to fit together, deducing a clear picture on the status of a network from all those packets becomes a monumental task.

Diverse Protocols and Functions

  • Modern networks are characterized by a myriad of protocols and functions. To properly troubleshoot such a wide variety requires a meticulous and patient approach.

Intermittent Failures

  • The unpredictability of intermittent deviations in control and user planes adds another layer of complexity to any manual analysis.

Making matters worse is that this type of manual analysis is most often called upon when timely responses are required — like network outages, server failure warnings or other major performance issues. The urgency of these network problems has amplified the need for advanced monitoring tools and modern methodologies across the telecommunications industry.Because of this, the roles of automation and machine learning have become crucial for network troubleshooting.

Automated Troubleshooting Enhancements for the Entire Network

Using manually generated PCAPS for individual network interfaces alone is like attempting to decipher a novel by only reading a dozen random pages at a time. You might connect some fragments of the story, but the full narrative, with its nuances and critical details, remains elusive.

Now, imagine you’re tasked with finding and fixing a plot-hole in the novel. It’s going to take time to not only find where the error occurs, but fix it in accordance with the rest of the narrative.

Staying with the novel analogy, what automation and machine learning provide is the ability to decipher the whole story instantaneously using the knowledge of thousands of libraries worth of novels. From there, it not only reconstructs the book for the reader, but it also offers a helpful Cliff’s Notes summary in visual form.

In network troubleshooting terms, this type of tool:

  1. Automatically extracts data from PCAPs;
  2. Detects root errors and root cause using machine learning insights; and
  3. Delivers a topology visualization with interactive sequence diagrams that offer a more holistic view of the individual network components.

It’s faster, more thorough and delivers a clearer picture of what’s actually happening in the network than even the best manual analysis — and it leads to better overall network performance.

But the best part is that this isn’t a hypothetical or an imaginary set of tools for the future, it’s exactly the type of automated approach to PCAP analysis that AGILITY provides right now.

A table showing how AGILITY approaches packet analysis
How AGILITY Approaches Packet Analysis

The Power of Network Topology Visualization

One of the guiding principles behind AGILITY is the belief that visual clarity is the gateway to comprehension. With all the complexities of modern networks, where abstract data and numerous variables intertwine, being able to tangibly "see" where the problem lies is the first step toward finding a solution.

In other words, a picture is worth a thousand packets.

That’s why AGILITY uses what it extracts from PCAPs to provide a Dynamic Network Topology that goes beyond textual data to offer a real-time visual representation of the network's intricacies.

  • Comprehensive Diagrams: Networks are vast, intricate entities. AGILITY paints a clear, comprehensive picture, making analysis more intuitive to access, monitor and analyze network issues faster.
  • Clarity Surrounding Differentiating Parties: By clearly marking the roles of various entities, the analysis process becomes streamlined, reducing chances of oversight.
  • Domain-specific Grouping: Segmenting network functions by domain ensures that call flows are organized and easily digestible.

A screengrab of AGILITY’s Dynamic Visualization
Seeing is believing when it comes to networks

AGILITY's emphasis on visualization empowers network professionals with a tool that simplifies complexity, making the intricacies of network troubleshooting more accessible and efficient.

But it doesn’t end there.

What are Interactive Sequence Diagrams?

In many cases, simple visualization is a great tool for identifying where a problem originates, but diagnosing an issue often requires a hands-on approach that a static view simply can't provide.

Once AGILITY automatically extracts the PCAP information to build a topology, it provides Interactive Sequence Diagrams designed to aid analysts in the faster triage of network issues.

What's So Special about Interactive Sequence Diagrams?

As a tool, Interactive Sequence Diagrams allow network administrators, engineers, managers, and analysts to troubleshoot with ease by interacting directly with protocols, packet data, and more.

Multi-protocol Layering

  • Given the multitude of protocols in modern networks, having them sequenced in a unified diagram ensures clarity when it comes time to identify an issue. This connected layering breaks down the complexities, making it easier to trace and understand network processes.

Advanced Filtering Tools

  • The interactivity of the sequence diagram extends to filtering so that network analysts can sort by both protocol of interest and by message type (request or response) to help detect network issues and determine the clear steps to fix it.

Call Flow Errors

  • As an extension of that filtering, analysts can quickly identify which errors were present in the call flow and identify the network function from which it originated.

This hands-on approach doesn't just improve understanding, it accelerates testing and problem-solving to create a more intuitive, user-centric approach to troubleshooting.

AGILITY’s Additional Benefits

No Need for Mapping Files

  • AGILITY's innovative approach means it can dynamically detect network topologies. Gone are the days of relying on outdated, static IP mapping files as navigational tools. Analysts receive access to real-time, accurate network representations.

Topology Diagrams for Both Originating and Terminating Parties

  • AGILITY constructs a topology diagram for both originating and terminating parties, ensuring that the network functions are grouped per domain, making it easy to connect network functions with call flows.

Always Improving with Machine Learning

  • By automating a significant portion of the analysis and troubleshooting tasks, machine learning ensures that the top talent can focus on solving the most critical network issues, all while optimizing network performance and enhancing user experience.

Solve Network Issues Faster

It’s only through automatic extraction from PCAPs, combined with machine learning insights for root error and root cause detection and topology visualization, that network engineers and managers can streamline network troubleshooting and dramatically improve on the efficiency of traditional manual analysis methods, all while improving overall network performance.


While PCAP analysis has historically been the cornerstone of network troubleshooting, it's a method that demands a fresh approach to keep up with dynamic digital networks. AGILITY, with its automation prowess, machine learning-driven insights, and dynamic visualization, is transforming this outdated process with the exact solutions it needs to move forward.

For telcoms, this isn't just about simplification; it's about elevating the entire operations of network troubleshooting to match the progress of modern networks.

We believe AGILITY is at the forefront of this transformation, and we want you to believe it, too. What better way to do that than with a firsthand experience? Take the leap into the future of network troubleshooting with our free trial.