Packets have always contained the answers.
Now everyone can ask the questions.
By Ron Nevo, Chief Technology Officer, cPacket Networks
Over thirty years as an engineer, I've witnessed several moments when a powerful technology suddenly became accessible to everyone. Each of these shifts followed a similar pattern: a capability that once required deep expertise became simple enough for anyone to use – and the impact was transformative.
I remember the first time I saw an early web browser in the 1990s. A coworker clicked on a hyperlink in Mosaic and images appeared right on the screen. No arcane commands, no downloading through text interfaces. The Internet, which had existed for years, suddenly felt like something ordinary people could use. Years later, I had a similar moment with the first iPhone when I pinched the screen to zoom into a web page. In that simple gesture, it became clear that the entire Internet was at my fingertips. The web had escaped the computer.
These breakthroughs didn’t invent new technology, so much as they democratized existing capabilities. Booking travel went from calling an agent and waiting days, to scrolling through options on a PC for hours. Today, mobile apps make it possible to compare flights, hotels, and prices in minutes — or soon book an entire vacation in seconds with AI agents.
I believe we're at a similar inflection point in IT operations. Packet data has always been the most complete record of network activity – the ground truth behind every transaction, every latency spike, and every failed connection. But understanding that data has historically required specialized expertise. Today, with the emergence of agentic AI and intelligent packet analytics, that barrier is disappearing. The same packet-level insights once reserved for a handful of experts are becoming accessible to teams across enterprise IT organizations.
The Packet Paradox
For decades, network engineers have relied on packet capture and tools like Wireshark to understand what's really happening inside their networks. By examining packets directly, engineers can see every request, every response, every delay, and every failure. With the right level of analysis, packets will not only tell you what happened, when it happened, and where it happened – but increasingly why it happened.
As networks have grown faster and more complex, it has made packet analysis harder to scale. In the early days of networking, engineers might have analyzed links running at 9600 bps. Today, enterprise networks routinely operate at 100, 400, and soon 800 Gbps. Yet the ability to interpret packet data has not expanded at the same pace.
Traditional packet analysis tools were designed for networking specialists who understood how to navigate complex interfaces, filters, and protocol decoders. To extract insights, engineers had to know exactly where to look, what to look for, and how the tool defined each element. Is it an IP address or an endpoint? A client or a server?
This has created what I call the packet paradox: the most complete and authoritative source of operational truth already exists in packet data, but only a small number of specialists have the tools and expertise to use it effectively. In large IT organizations with hundreds of operations staff, only two or three engineers truly know how to capture and analyze packets in depth. Everyone else must rely on logs, dashboards, or indirect telemetry — and often escalate issues when those tools fall short.
For years, this limitation was accepted as part of operating modern networks. Packet analysis was simply too complex to scale across large IT organizations. But that assumption is beginning to change. Advances in agentic AI are transforming how engineers interact with packet data — removing the expertise barrier that has long limited its value.
The AI Agentic Transformation
When I first saw how Model Context Protocol (MCP) and agentic AI allow people to interact with packet analytics, I felt that same spark I felt watching Mosaic load images, or pinching to zoom on that first iPhone. Agentic AI will completely change the dynamics of IT operations by democratizing packet intelligence and transforming how engineers interact with observability tools.
This shift represents a fundamental change in how packet observability can be used inside an organization. Instead of manually navigating PCAPs or constructing complex filters, users can simply describe what they're looking for in their own words. Agentic AI workflows, built on MCPs and embedded domain knowledge, automatically translate those questions into packet-level analysis – examining flows, dependencies, and service interactions to identify the underlying cause. With natural language interfaces, AI translates between human intent and network evidence.
The impact is profound. A Level 1 support engineer investigating a user complaint can now access the same packet-derived insights that once required escalation to senior network engineers. Instead of asking, “Can someone analyze a packet capture?” they can triage issues and pinpoint the right escalation path by asking “Is this a network issue or an application issue?”
In practical terms, packet observability is no longer a niche troubleshooting technique reserved for major incidents. It becomes a shared operational capability across IT. Network engineers, SREs, infrastructure teams, and support staff can all work from the same ground truth.
Just as web browsers made the Internet accessible to everyone and smartphones put computing in everyone’s pocket, agentic AI is now making packet observability accessible to every operations team.
Evolving IT Workflows: Example from the field
Since we released our first MCP servers to early adopter customers last year, I've seen how they're using agentic AI with packet analytics. What I'm witnessing is the beginning of a transformation — one that I believe will finally make the value of packets accessible to IT operations teams at large. Most notably, these early adopters have become significantly more proactive, identifying and resolving issues before users even call to complain.
Here is a simple, yet illustrative, example of how AI workflows are changing the way real network operations teams perform root cause analysis.
One of our customers operates an employee store offering special pricing on services and items. When users complained that the service was too slow to access, the service owner posted a question on internal pre-support channel asking for help. The director of network operations used their chat interface to get instant access packet-level data through our MCP. In a mere 5 minutes between meetings, they verified the issue and pinpointed the cause as servers running out of resources. In this case, they submitted the issue for a third-party provider to resolve – and the NetOps Director went back to their next meeting.
This story isn’t dramatic, but it exemplifies a major transition in daily workflows. Instead of opening a ticket, assigning people, and taking hours to identify root cause and resolve, network operations was able to triage and find the root cause in minutes – while keeping up his normal tasks. AI did the heavy lifting and analysis of the metadata and metrics from the packet data they already have.
What This Means
For decades, packet data has been the most reliable source of truth in network operations. Every transaction, every delay, every failed connection ultimately appears in the packets moving across the network. Yet despite its value, packet analysis has remained largely confined to a small group of specialists equipped with complex tools and deep protocol expertise.
What is changing today is not the importance of packets — but their usability. At cPacket, we’ve spent decades building platforms that capture and analyze packets at the scale modern networks demand. By introducing MCP servers and agentic AI workflows, we are taking the next step: making the insights contained in packet data accessible to everyone who needs them.
It is worth mentioning that operations teams have access to a variety of network, application, and other telemetry. While agentic models thrive at connecting diverse data sources, MCP servers integrate packet insights with the tools that organizations rely upon -- ITSM platforms like ServiceNow, SIEM solutions like Splunk, and observability platforms like Datadog. The goal isn't to replace the existing stack, but to enrich it with the network ground truth that only packets can provide.
When every team and every tool can access the same ground truth, collaboration improves and resolution times shrink. Support engineers can triage issues faster. Infrastructure teams can identify the real source of latency or failure without hours of manual investigation. Network teams can move from defending themselves in war rooms to providing definitive evidence of what is actually happening across the environment.
The Internet became accessible when browsers simplified how people interacted with it. Mobile computing expanded when smartphones made powerful technology intuitive to use. Packet observability is now entering a similar phase.
