How to Implement Automation in Network Management

I created a Python bot that can check the status of interfaces on my Cisco router. I started by using a single router to make sure everything worked correctly. The bot reads the router information from a file called devices.yaml, which has details like its IP address, username, password, and device type. Python stores this information so I can tell the bot which router to work on. This makes it easy to start small and test the bot without affecting multiple devices. Next, the bot connects to my router using SSH with a library called Netmiko. Netmiko allows Python to log into the router, send commands, and get responses automatically. I use the command show ip interface brief to see which interfaces are up or down. The bot then analyzes this information and tells me which interfaces are not up, saving me the time of checking manually. When the bot finds an interface that is administratively down, it asks me if I want to enable it. If I say yes, it automatically sends the command to bring that interface back up. For interfaces that are down for other reasons, it simply reports them without making any changes. The bot also handles connection problems safely, so if my router is offline or unreachable, it won’t crash or stop working. I can make this bot interactive to explore more ideas. For example, I could modify it to let me choose multiple actions for the interface, like shutting it down, restarting it, or checking traffic statistics. I could also add a summary at the end showing which interfaces I fixed and which ones still need attention. Starting with just one router helped me learn how automation can save time and reduce errors. What’s one network task you wish you could automate in your own environment?

I just built my first OpenClaw project! I needed to get my hands on it and build an agent with it - and I did - it's called NetClaw — an AI network engineering agent that operates at CCIE-level depth across routing, switching, security, QoS, MPLS, IPv6, multicast, wireless, and more. 30 skills. NetClaw is built entirely on OpenClaw using what I'd call the "tools as skills" architecture. Each skill is a structured knowledge document that teaches the agent how a network engineer thinks — not just what commands to run, but when to run them, what to look for in the output, and what to do next based on what it finds. The agent connects to live network devices through pyATS skills which abstract the MCP server, executes real show commands, parses real output, and makes real engineering decisions.                  I'm writing this post while simultaneously talking to NetClaw in the VibeOps Forum Slack workspace. I asked it to analyze routing tables and interface states from a live device, generate a Draw.io topology diagram, and create an  image of the network state — all through a Slack message. It did all three. No portal. No ticket. No context-switching. Just a conversation with an engineer that never sleeps.                                                                                    Here's what strikes me about this:                                          We've been automating the wrong layer. For years, network automation has focused on pushing configs and collecting data. Template engines. YAML files. CI/CD pipelines for network changes. All valuable. But they automate the execution — not the reasoning. NetClaw automates the reasoning. It doesn't just run show ip ospf neighbor - it knows to check hello/dead timer mismatches, area ID conflicts, MTU issues causing EXSTART stuck states, and passive interface misconfigurations. It follows the same OSI-model troubleshooting methodology that a CCIE would use.                                                                  Skills are composable. The topology discovery skill feeds into the diagram skill. The security audit skill references the compliance skill. The troubleshooting skill pulls from health checks, routing analysis, and log      inspection. This isn't a monolithic runbook — it's a network of knowledge that the agent traverses based on context.                                                              The conversation is the interface. There's something profound about being in a Slack channel, asking a question in plain English, and getting back a security audit with findings categorized as Critical, High, Medium, and Low complete with CVE benchmark references and specific remediation commands. The barrier between "I wonder if..." and  "here's the answer" has collapsed to the speed of thought. https://lnkd.in/eF-xgM8i

⚡ Telecom Automation Tools Roadmap Telecom networks are becoming too complex, too distributed, and too software-driven to rely on manual operations. Automation is no longer optional, it’s the core operating system of modern networks. From CI/CD pipelines and cloud-native provisioning to AI-driven RAN optimization and event-based orchestration, automation now defines how telecom runs. Here’s a clear roadmap of the essential tools every telecom engineer should master to build, operate, and scale next-gen intelligent networks 👇 🔹 Orchestration & CI/CD for Networks Tools like Helm, Kustomize, ArgoCD, Jenkins, GitLab CI automate packaging, deployments, config rollouts, and multi-cluster updates across large-scale networks. 🔹 Scripting & Programming Python, Bash, Go, Ansible, Terraform, Netconf/RESTconf form the backbone of: -config generation -telemetry parsing -test automation -cloud provisioning -device onboarding 🔹 Infrastructure & Network Automation Ansible + Terraform automate multi-vendor device configs, network provisioning, and infrastructure orchestration across cloud, edge, and telco platforms. 🔹 Messaging & Event Streaming Kafka, RabbitMQ, NATS power real-time telemetry pipelines, event-driven orchestration, and microservice communication across distributed networks. 🔹 Observability & Telemetry Prometheus, Grafana, ELK Stack enable deep visibility through metrics, logs, traces, QoS indicators, anomaly signals, and root cause data. 🔹 AI/ML Tooling for Network Automation LLMs, TensorFlow, PyTorch, Python ML Libraries empower networks to: detect anomalies, forecast KPIs, automate RCA, make intelligent decisions in real time 🔹 RAN Intelligence & Open RAN Tooling RIC (Near-RT & Non-RT), xApps, rApps introduce real-time optimization, interference management, spectrum intelligence, and predictive control. 🔹 Telecom Orchestration Platforms ONAP, Nephio, EMCO drive service orchestration, multi-cluster lifecycle management, and E2E automation across 5G, cloud, and Open RAN deployments. 🌐 The Future: Converged Automation Mastering telecom automation isn’t about one tool, it’s about understanding the full ecosystem. From scripts and CI/CD pipelines to RIC intelligence, telemetry systems, and AI-driven closed loops. The more these tools work together, the closer networks move toward true autonomy, resilience, and real-time adaptability. The future of telecom will be built, and automated, by those who understand this ecosystem. 🌍 Follow Abhishek Singh for deep insights on how AI, automation, and next-gen networks are shaping the future of connected intelligence. #Telecom #NetworkAutomation #CICD #NetDevOps #AI #AIOps #5G #CloudNative #OpenRAN #OSSBSS #DigitalTransformation #EdgeComputing #Automation #InfrastructureAsCode #FutureOfNetworks

15 lines of YAML. That's it. I built an Intent-Based Networking platform that takes a simple intent file - source, destination, latency requirement, diverse paths - and turns it into BGP config deployed across 4 routers with BFD protection and mathematically proven path diversity. You define what you want. The platform figures out how to make it happen. The secret sauce? Microsoft's Z3 SMT solver. Traditional shortest-path algorithms like Dijkstra can't express "find me two paths that share zero failure points." Z3 treats this as a constraint satisfaction problem and solves it in milliseconds. It parses your intent, runs Z3 to compute optimal diverse paths, generates the BGP configs with route-maps, deploys everything via SSH to Cisco C8000V routers, and verifies the BGP/BFD sessions come up clean. What used to take hours of manual config and verification now takes minutes. But here's the part I'm most excited about - I can simulate failures before they ever happen. Run ibn what-if --fail-node Core1 and if it comes back "No solution available"? That's a single point of failure detected before it takes down your network. No more 2am outage firefights because someone missed a redundancy gap. I used Claude Code (Anthropic's AI coding assistant) to research and design the entire platform. About an hour of back-and-forth conversation to work through the architecture, understand Z3 constraint formulation, and map out the implementation. Then I built it. Still plenty more to add - NETCONF/RESTCONF support, a web dashboard, multi-vendor templates, and continuous compliance monitoring. But we're off to a solid start. This is where network engineering is headed. Less CLI memorization, more intent-driven automation. Define the outcome. Let the system handle the complexity. The repo is in the comments 👇🏾 #NetworkAutomation #IntentBasedNetworking #Python #BGP #ClaudeCode

The Next Wave in CCIE-Level Network Automation Traditional CLI-only designs served us well—but the CCIE of 2025 is fluent in intent and APIs. Model-Driven Telemetry: Move beyond SNMP polling; subscribe to live data feeds for real-time change detection. YANG + gNMI/gRPC: Push your network configurations programmatically—no more line-by-line CLI copy/paste. Closed-Loop Assurance: Leverage open-source toolchains (e.g. Telegraf → Prometheus → Grafana → Ansible) to detect drift and auto-correct. Your jump-start plan: Spin up a virtual IOS-XE device in EVE-NG or Cisco DevNet Sandbox. Model one interface change via YANG + gNMI. Wire it into a basic Python script that verifies success—and then tear it down automatically. 💬 Who’s already building intent-driven workflows for your lab or production nets? Share your wins (or gotchas)! #CCIE #NetworkAutomation #IntentBasedNetworking

Simplify Network Management with Cisco Meraki Switches This technical paper by Cisco Systems, Inc. outlines 10 powerful ways Meraki switches streamline enterprise network operations - from zero-touch deployment to intelligent cloud management. Key highlights include: • Centralized port management from a single dashboard • Remote cable testing and live packet captures • Rogue DHCP detection and port-based access control • Seamless firmware updates with no manual intervention • Built-in analytics for identifying bandwidth and power usage trends This document is an excellent resource for professionals looking to enhance efficiency, visibility, and security in their switching infrastructure through Meraki’s cloud-managed approach. Author: Cisco Systems, Inc. Highly recommended reading for network engineers, architects, and IT managers seeking next-generation switch management simplicity. What’s your experience with Meraki switches in large-scale deployments? Do you find cloud-based management a net benefit in enterprise environments? Let’s discuss #Cisco #Meraki #NetworkAutomation #Switching #CloudNetworking #NetworkManagement #ITInfrastructure

#AutoCon3 From Clicks to Code: Optical Network Automation Journey at GARR Matteo Colantonio, Optical Network Engineer at GARR, shared their journey to automate the optical network at GARR, an Italian research network. They started by looking at widely adopted tools, including Ansible. It worked to help the team update 92 transponders However, they realized Ansible has scaling limitations when things get complex. In the optical layer, some devices don’t support NETCONF so you have to develop a module. If you have simple procedures, such as pushing config, Ansible is fine. But as you get into complex logic to configure services, not just boxes, you may want to reconsider your life choices. They also tried working with vendor controllers. Provisioning optical circuits can take 40 to 50 clicks across 4 GUIs. The vendor controllers sort of worked. It didn’t replace all the manual clicks. They still had to do manual pre-provisioning work, create cross-connections on some cards, and fix non-meaningful names, and add descriptions. They also don’t have a single optical line system, so the controller API only works with one vendor. The Workflow Orchestrator Framwork They discovered Workflow Orchestrator developed by SURF, a Dutch research network. It’s been open-sourced and lets other organizations adopt the framework. workfloworchestrator.org What do you get out of the box? -It’s a framework, not a turnkey solution, but it lets you define your network services or entities, or domain models for your organization -It lets you track instances -It defines clear procedures, or workflows Everything is stored and tracked in a database for object and relational mapping You start by defining building blocks, such an optical fiber. There’s a fiber name, terminiations, OSS ID, etc. You turn these blocks into Products to manage the lifecycle of a Block. Workflows make things happen. It uses Python functions, so you can do whatever you want. It can handle very complex logic. They went from 50 clicks and 15 to 20 minutes to an automated workflow that takes 50 seconds. Was it Easy? No. It’s harder than getting started with Ansible, but it was worth it. From this project they got: -Central service definitions -Consistent execution of service management -They have a consistent architecture -If new hardware comes in, they can modify clients without having to modify workflows Key Take-Aways: 1. If you want to develop a scalable, maintainable solution, the best option is to go with abstract and composable models, and to go with stateful instances of these models. 2. If you want your network to be programmable, use the devices’ programmable interfaces and YANG models, not just CLI 3. Make sure your transformation is sustainable. Automate one service at a time to nudge people out of their comfort zones

Recent Breakthrough Advances in AI applied to Network Operations by Ex Google Verizon Labs How Aerloop Transformed Network Operations with NetAI Aerloop, a mid-sized ISP, faced a critical challenge: recruiting and retaining skilled network engineers to manage its increasingly complex infrastructure. Led by John Baptist, a highly respected industry veteran, the team was overwhelmed by an avalanche of uncorrelated alerts from legacy tools based on SNMP, logs, NetFlow etc The chaos resulted in missed critical issues, delayed responses, and rising customer complaints. Dissatisfaction drove churn and revenue losses. Despite their best efforts, the team couldn’t keep up, and traditional tools fell short. Aerloop needed a breakthrough. The Game-Changing Solution Aerloop turned to NetAI, a unified, AI-powered platform that integrates data from all sources and delivers real-time root cause analysis. At its core is the Graph Neural Network (GNN)-based Network Incident Engine, which maps network relationships, identifies dependencies, and uncovers root causes with unmatched precision. John tested NetAI in a lab trial. The results were eye-opening: in a simulated cascading failure, NetAI pinpointed the root cause—a misconfigured core router policy—and recommended actionable steps. This process, which would have taken hours with existing tools, was completed in minutes. When deployed in production, the results were transformative: Alarm Backlog Reduction: NetAI cut through noise, prioritizing critical issues and clearing the alarm backlog in days. Upto 90% Faster Resolution Times: Accurate root cause analysis reduced incident response times by upto 90% Fewer Complaints: Improved network reliability led to a sharp decline in customer complaints. Higher Team Morale: By automating mundane troubleshooting, engineers could focus on strategic tasks. One notable incident involved a widespread outage. While traditional tools generated hundreds of unrelated alerts, NetAI identified the root cause—a faulty fiber link—and provided remediation steps, enabling Aerloop to restore service before customers noticed. The Secret: GNN-Powered Automation Unlike conventional tools, which struggle with complex, interconnected networks, NetAI’s GNN engine excels at analyzing dependencies. This ensures the team resolves the root cause, not just symptoms, reducing noise and enabling proactive responses. A Unified Platform for Operational Excellence NetAI replaced Aerloop’s fragmented setup with a single, integrated tool. Its ability to unify SNMP, logs, NetFlow, and anomaly detection simplified training, reduced inefficiencies, and enhanced productivity. Reduction in Churn: Improved reliability retained more customers. Revenue Stabilized: Better customer retention directly boosted financial performance.e For Enterprises, MSPs, ISPs facing similar challenges, NetAI offers a clear path forward: unifying operations, automating the mundane, and focusing on what matters most.

🤖 Let's talk about AI and network engineering... Look, I get it. Every vendor is screaming about "AI-powered" this and "machine learning-driven" that. It's exhausting. But here's the thing – AI is actually making my team way more effective, and I'm not talking "someday in the future". I'm talking about right now, today, with tools we're already using. I just dropped a new blog about how I'm navigating AI adoption as a network manager. This isn't hype or vendor marketing – it's real talk about: ✨ Where AI actually saves us hours (documentation, anyone?) ⚠️ Where it falls flat and you absolutely need human expertise 🎯 How to get your team excited about AI without freaking them out 🚫 The drawbacks nobody talks about (yes, there are several) My favorite example? We used AI to parse switch configs and find VLAN misconfigurations. Took 15 minutes to generate the script vs. what would've been a full day. Then we spent 2 hours customizing it. That's the sweet spot – AI does the grunt work, humans add the expertise. The reality? AI isn't replacing network engineers. But network engineers using AI are absolutely going to outpace those who don't. As managers, we need to help our teams navigate this shift. I also get into the tough stuff: • How to handle team members who think AI will take their jobs • Why you can't just blindly trust AI output (spoiler: hallucinations are real) • Keeping core networking skills sharp while embracing new tools • Real examples from our team's wins AND failures Whether you're managing a network team or just trying to figure out where AI fits in your daily work, I think you'll find this useful. Check it out: https://lnkd.in/e7HkYz9j PS - If you dig this kind of content (technical deep dives + real management experiences), I'm sending out a monthly newsletter now. Sign up and grab my free Network Engineer's Quick Reference Guide while you're there. No spam, just practical stuff for network folks. What's your take? Are you using AI in your network engineering work? What's working? What's been a total miss? Drop a comment – genuinely curious to hear what others are experiencing. #Blog #Tech #Content #NetworkEngineering #ArtificialIntelligence #TechnicalLeadership #NetworkManagement #AITools #TeamDevelopment

🚀 I'm thrilled to share some insights from my recent experiences with using Nephio to orchestrate the lifecycle of Telco CNFs. 1️⃣ Hosted Kubernetes Control-Plane with K0smotron: I've been leveraging K0smotron to host Kubernetes control planes on Proxmox. This setup offers a strategic alternative by deploying the Kubernetes control-planes centrally rather than at the edge locations. This centralization can lead to significant cost savings by reducing the number of servers needed for ETCD quorum. 2️⃣ Kubernetes as a Network Router Control-Plane: In this architecture, Kubernetes is not just handling container orchestration—I'm using it as the control-plane for configuring network routers. This method simplifies network management and ensures seamless integration with the existing infrastructure. 3️⃣ Dynamic VLAN Configuration with DaemonSets: To maintain efficient and consistent network segmentation, I've implemented a DaemonSet that automates VLAN configuration across remote worker nodes. This automation is key to managing complex networks with ease. Built on Proxmox as the hypervisor and powered by Nephio Release 3, this architecture is at the forefront of pushing Kubernetes beyond its traditional use cases. Opinions shared in this post are my own and do not reflect those of my employer. #Kubernetes #K8s #Networking #DevOps #Automation #CloudComputing #Infrastructure #Nephio #ProxMox #K0smotron #GitOps https://lnkd.in/esq8gni3