--- title: "4 Steps to Build Trust in Network Automation" id: "26042" type: "post" slug: "build-trust-network-automation-ip-fabric" published_at: "2026-04-29T20:13:40+00:00" modified_at: "2026-10-26T21:21:03+00:00" url: "https://ipfabric.io/blog/build-trust-network-automation-ip-fabric/" markdown_url: "https://ipfabric.io/blog/build-trust-network-automation-ip-fabric.md" excerpt: "Learn how you can build reliable network automation and AIOps—starting with the right data." taxonomy_category: - "AI & automation" taxonomy_post_tag: - "AIOps" - "MCP server" - "Network automation" --- # 4 Steps to Build Trust in Network Automation Learn how you can build reliable network automation and AIOps—starting with the right data. [https://ipfabric.io/author/daren/](https://ipfabric.io/author/daren/) [Daren Fulwell](https://ipfabric.io/author/daren/) Field CTO Published 29 Apr 2026 Updated 26 Oct 2026 For decades, network operations teams have followed a single mandate: run a network the business can depend on. This hasn’t changed much over the years, but what has changed is the level of complexity. Modern enterprise networks are always in flux, with near-constant updates across data centers, cloud platforms, SaaS services, and edge environments. All together, this infrastructure powers the applications and services that keep the business running. But if you lose track of a device here, or a dependency there, it gets increasingly difficult to maintain an accurate understanding of what’s actually going on in your network. Even the most experienced engineers can’t always predict how a given change will impact the customer experience. But there is a way to know—and it starts with better [network visibility](https://ipfabric.io/blog/ip-fabric-hybrid-multi-cloud-network-visibility/) . Transcript I wanted to show you something, some experiments we’ve been doing on using MCP as an interface into IP Fabric’s data to really enable some interesting use cases very much focused on the kinds of things that you do or I do as a network engineer. I’ll very quickly buzz through the first one. We’ll talk about root cause analysis. Now, what we’ve got here is a situation with IP Fabric where over two snapshots, we’ve got a path from a host to uh AWS, and it’s changed. Something’s changed uh during those two paths. One one time it was going this way towards this firewall and out. The other time it’s going uh as you can see down here into the MS network and across means that the routing’s changed between the two and we’ve got all the information. But rather than dig into it yourself to go find out, what we can do instead is ask um ask Claude to help us out here. So if I just switch this to uh show you what I’ve done with Claude. Claude is is now attached to an MCP server which is hanging off the back of IP Fabric and it’s able to query the information and then reason about it. So I’ve set up a prompt here as you can see said that this these two devices are in the network one’s on prem one’s in the cloud some change has happened a particular date go find out what happened and so what it’s actually done here um if you if I just quickly scroll this through you’ll be able to see that it’s got the information about the two snapshots it’s traced the path in the two similar to what we saw in the uh visualization before but then it actually looks at the reasons why that’s happened. So it looks at the routting tables on the devices, understands that there’s the change, identifies what that change is, and it digs into the way that that change has echoed through the network and summarized it. Right? So it’s actually produced an output here which essentially says that route is propagated from AWS through um BGP into a firewall into OPF and it’s understood all of that and it’s produced me a report and that report details all of the information that’s been used in order to gain uh an understanding of what’s happened. And what it’s done is created me an executive summary as you can see. And then down at the bottom here, it’s given me some recommendations, reviewing redistribution policy, looking at the design intent, consider root summarization. So, it’s produced here a report that basically is is um giving me that root cause analysis that I’ve been looking for. That would have taken you or me as a network engineer uh several hours to get to the bottom of. And because we have the data in IP fabric and because we can expose it the right way, Claude is able to do this for us in minutes. If this is interesting um and you’re in Amsterdam this week, go and see our team um at Cisco Live. It’s in booth B 04 in the world of solutions. They can show you this and other examples like it of things that you’re be enabled to do using the unique data set that’s in IP Fabric’s digital twin. See you there. *Learn more about IP Fabric’s enterprise-grade [MCP server](https://ipfabric.io/blog/ip-fabric-mcp-server-for-enterprise-aiops/) .* ### Demand More From Your Network Data When a NetOps team feels like they’re losing control of their network, automation is often their first attempt to fix it. They’ll take a “divide and conquer” approach: automating what they can, so they have more time to work on what they can’t. This strategy is sound in theory, but in order to succeed, they need to build automation using [the right data](https://ipfabric.io/blog/network-discovery-build-network-source-of-truth/) . And so, we’ve circled back to our original problem: not having an accurate understanding of the network. The same concern extends to [AIOps](https://ipfabric.io/unlock-aiops-with-ip-fabric-enterprise-mcp-server/) . AI agents, like humans, need the right data to get the right result. Is your data consistent? Is it complete? Is it up to date? If you answer “no” to any of these three questions, your AI agent is as likely to make mistakes as any human employee. Transcript AI does not magically fix bad data. So often times in our modern environments, we’re dealing with multiple vendors, whether those are on prim in the cloud, different vendors for routing, switching, wireless, uh, firewall. So what we end up with is with a hodgepodge of tools oftent times with different gooies for management, different operating systems, and even different APIs, logs only show a small portion of the picture. Whereas with IP Fabric or network snapshots, you’re getting a complete view of how that network is architectured, configured, and operating. Our mission at IP Fabric is to democratize your network data. We try to empower everyone in the organization, whether they’re admin, engineer, automation engineer, AI developer. We try to democratize that data so they all can have that power to do that automation and make those consistent decisions and understand their network infrastructure. *Learn more about how to [build for network automation and AI](https://ipfabric.io/blog/total-network-operations-network-digital-twin-ai/)* with IP Fabric. ### Close the Network Visibility Gap The shift from manual operations to automation—and eventually, to [autonomy](https://ipfabric.io/blog/agentic-ai-autonomous-network-digital-twin/) —is a progression that requires a continuously updated understanding of the network. AI agents act on context. If that context is trapped in people’s heads, the AI agent’s output will reflect that. This is where [network digital twins](https://ipfabric.io/blog/total-network-operations-network-digital-twin-ai/) come in. Rather than cobbling together a patchwork view from spreadsheets and fragmented tooling, organizations can trust a network digital twin to be an authoritative simulation of network behavior at a given point in time. Better yet, it’s modeled in structured data so it can serve as a common cognitive substrate for human engineers and AI agents alike. When your teams and tools are all informed by the same source of truth, it builds a foundation for more reliable and resilient operations. In other words, this shared visibility translates directly to lower risk—and cost—over time. Transcript Hi, Darren Fwell from IP Fabric here again um with another example of some of the work that we’re doing with our experimentation around MCP and exposing IP Fabric’s knowledge and insight of your network. Um what we’re doing here today is we’re looking at a design validation. So, as network engineers, obviously we’ve all seen many network design documents containing tables that look something like this. Um, in this case, you’ve got information here about layer 2 connections between switches. You’ve got layer three connections and their IP addresses. You’ve got the the gateway settings, and you’ve got the uh VRP configuration information, for example. You could have a bunch of things here, but what we want to be able to do is um go to our network and validate that actually the network has been deployed the way you would expect it to. Now um as you can see from our IP fabric snapshot here, we have information about that location is that you can already see there are things missing obviously with the resilience back back here in the switches and that firewall there the the VPN firewall clearly um isn’t connected to anything. So what we’ll do is we’ll just have a look at the the next snapshot which shows um what happens once some changes have been made. And now you can see my firewall is connected to the rider network. It’s also connected out to AWS here. Um and I’ve got some um connections as well, some redundant connections from uh my switches out here which I didn’t have previously. So all good. But are they configured the way that we need them to be? Well, what I’m going to do, I’m gonna te get someone to go and answer me that question without me having to do it myself. Um, let’s uh switch across and show you what we asked uh Claude to do for us. So, Claude is connected via MCP to our IP fabric snapshot. Um, and what we’re going to do is we’re going to ask it to validate that design uh has been implemented as expected. the layer two uh layer three the uh first stop resolution protocol is all configured as it should be and that everything is connected to the right interfaces and then we’re going to ask it to to basically summarize that for us and uh and and tell us what needs to be remediated. So it takes the information from the snapshot. It takes which it’s you can see it’s found here. You takes the information from the spreadsheet and starts querying IP fabric in order to validate the switch ports, the IP interfaces, the VRP config and the neighbor topology. And some really interesting um detail here when you dig into it. Um and here you can see it’s actually completed the analysis. Um it’s shown that these are all the trunk links. It pairs those against the uh the which port configs. Um it’s gathering information here about which ports are connected to which and so on. Going through and doing lots of validation. You can see lots of ticks here, some crosses. Um you can see it’s checking all kinds of information and then it’s got all the data it needs. So let me open the resulting report. that comes from that. And it was this one, the Paris 1x validation report. And if I open this one up, you can see that it’s actually put together in markdown a full report of everything. And it’s saying here, Paris 1x has been validated against the design spec. Um, layer 3 infrastructure and SVI are correctly implemented, but there are significant discrepancies in layer 2 and in the VRRP config. and it proceeds here to to detail all of those here for you. So, here’s your tables with the information in um what’s good, what’s bad. Um you can see VRP, everything’s the group numbers are all incorrect and so on. But what it also does is adds our remediation actions for us. We didn’t even ask it to do that. Uh but you can see here the Arista config for those switches in order to go fix that information and a little summary at the bottom here to state the the percentage of compliance as well. So all of that done without any inter interaction with me the user um it’s gone ahead and and used its knowledge and understanding of networking in order to produce that. Now of course we wouldn’t take that verbatim. We would of course check that through, but you can see a significant amount of time been saved just bringing all that data together. If that use case is interesting um and if you’re you want to know more about the the things that IP Fabric are doing with uh MCP and you’re in Amsterdam this week, go to to our booth at Cisco Live, the World of Solutions booth B04. the team are there um demoing MCP and demoing um the solution as a whole. Um just let them know I I sent you. Take care. *Get a personalized [demo](https://ipfabric.io/get-a-demo-of-ip-fabric-enterprise-mcp-server/) of IP Fabric’s MCP server.* ### Build For Trust, Not Speed Many IT organizations are racing to embrace shiny new tech, but the truly forward-thinking leaders are the ones taking a step back and focusing on the quality of their network data. Once you’re confident in that data, it’s also important to think strategically about where AI makes the most sense in your network. Your infrastructure may vary in its complexity, with different areas carrying different levels of risk. Think of it as a “crawl, walk, run” approach. By earning confidence in lower-risk areas of the network first, your team can get the evidence you need to expand automation further—along with the judgment to know [where not to](https://ipfabric.io/blog/network-ai-predictions-network-digital-twin-2026/) . ### Settle In For the Long Game Autonomy isn’t a feature you switch on; it’s a destination you build toward. For decades, the job was simple: keep the lights on. That job has quietly become something far more demanding, and AI tools can only help if they’re built using data that’s current, complete, and consistent. The teams that figure this out early will gain something you can’t buy: the confidence to make changes quickly, knowing the network will behave the way they expect it to. *This article was originally published in [TechIntelPro](https://techintelpro.com/guest-articles/4-steps-to-build-trust-in-network-automation?utm_campaign=378945778-2026%20MCP%20server&utm_content=568665319&utm_medium=social&utm_source=linkedin&hss_channel=lcp-17940755) .* ## Related Resources AI & automation ### [AI Is Turning Data Governance Into A Competitive Advantage](https://ipfabric.io/blog/ai-data-governance-as-a-competitive-advantage/) Consider these 3 questions on data governance before adopting a third-party AI solution. AI & automation ### [The Network Data Problem Slowing Down Enterprise AIOps](https://ipfabric.io/blog/aiops-and-network-autonomy-network-digital-twins/) Why Does AI fail on fragmented data? And how can network digital twins help to build a foundation for network autonomy? AI & automation ### [Network Intelligence & AI-Ready Data](https://ipfabric.io/blog/network-intelligence-and-ai-ready-data/) How does a network digital twin help you to achieve AI-ready data?