Network Automation Tools

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

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

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.

Digital Ocean's Automation Tool Stack It's the final day of #AutoCon2. Mircea Ulinic starts the day talking about the tools Digital Ocean uses for automation and how they integrate. Environment: 15 data centers in 9 global regions, with a global backbone Tools in the stack: Data Collection: -NetBox -Peering Manager -IRRD Config management: -Salt -Tooling integration Monitoring: -Prometheus -Alerta -Event-driven orchestration “Getting the data right is the most difficult task in network automation, and you can’t get it right if you do it by hand.” Digital Ocean uses Netbox reports to validate data against actual devices. Peering Manager Peering Manager provides data for external connections, such as IX peers. He encourages the audience to check it out! IRRd: They use it as an internal whois for peering hygiene. Salt: Digital Ocean uses this for configuration management and orchestration. Why? -It’s vendor-agnostic -It’s tried and tested in large environments -It has orchestration and event-driven capabilities -As part of daily ops, they deploy config changes frequently, so they need orchestration -It includes a Rest API (Reminder from Mircea: Don’t just focus on tooling. Focus on your methodology. Salt works for Digital Ocean. If it doesn’t work for you, find the tool that does.) Salt has Proxy Minions that manage devices. They run Proxy Minions in Kubernetes, deployed regionally. About 35% of their network devices are managed by Proxy Minions. For the other 65%, they have crafted their own Salt plug-in that runs on a server or computer, and connects to devices in multiple ways (NETCONF, HTTP, SSH, gNMI). This is an internal project that Digital Ocean maintains. Monitoring a Global Network Their monitoring approach is based on Prometheus. They maintain multiple Prometheus exporters, including a NetBox exporter. Exporters connect to devices, grab data from devices using SNMP, NETCONF, and gNMI. Exporters run in K8s, deployed regionally. Prometheus stores device data in a time series database. You can build a dashboard using Grafana, or tie the database to an alert manager. Alerta is a dashboard that aggregates alerts. Digital Ocean has integrated it with NetBox so that if they see an alert in the dashboard, they can quickly get information such as the name of device on the other end of the connection. There’s also a Jira integration for ticket creation. How Digital Ocean supports renting GPUs to customers: Digital Ocean offers GPU rental. That means spinning up dynamic VLANs per customer, configured on the switch, with automatic switch reconfiguration on a customer action. This would be impossible without network automation. What's The Future? If you want your business to survive in the next 5 to 10 years, start with automation now. Networks are getting so complex, you need to automate. And the earlier you invest in automation, the faster you can deliver new features to customers.

Automating Cisco IOS Configurations with Ansible In modern network operations, automation isn’t a luxury-it’s a necessity. This document, authored by Meraj Hassan, provides a practical and deeply technical walkthrough of how network engineers can use Ansible to automate Cisco IOS configurations efficiently and reliably. It covers: Setting up Ansible in Linux environments Building inventory and playbooks for Cisco routers Using tasks and roles for modular automation Real-world examples of QoS, ACL, and backup automation A brief introduction to Ansible Tower and DevOps integration This is an excellent resource for anyone looking to transition from manual configurations to scalable, repeatable, and version-controlled automation workflows. Document Author: Meraj Hassan – Keep Calm & Automation On! Have you integrated Ansible into your network automation workflow yet? Let’s discuss your favorite use cases below #NetworkAutomation #Cisco #Ansible #DevNet #Automation #DevOps #Networking #CiscoIOS #NetOps #InfrastructureAsCode

In the era of network automation, how do you manage your device configuration? Using the CLI was easy. We logged in and applied the configuration. There was basically only one way of doing it (excluding GUIs). With network automation, we have a plethora of options, let's explore some of them. 𝐕𝐞𝐧𝐝𝐨𝐫 𝐭𝐨𝐨𝐥𝐢𝐧𝐠 - There are tools like Cisco Catalyst Center, HPE Mist, and many others. These typically do everything from zero touch provisioning, to software updates, to assurance, to device configuration. While powerful, it can be costly and might not apply to your entire fleet of devices. 𝐀𝐧𝐬𝐢𝐛𝐥𝐞 - Ansible is one of the easier tools to get started with. It uses YAML syntax which is pretty easy to learn and human readable. The organization may already be familiar with Ansible for automating other infrastructure. It may struggle with more complex tasks and also has a history of modifying the core module which may break existing tooling. 𝐓𝐞𝐫𝐫𝐚𝐟𝐨𝐫𝐦 - Terraform is commonly used in public clouds. This is a benefit for teams that have a lot of workloads there. While it has its own domain-specific language, it's similar enough to JSON to not make it that steep of a learning curve. A benefit with Terraform is that it can be used for many things. If you need more functionality than what's in a provider, you should know that providers are typically written in Go. This makes it more difficult to extend functionality. Not all devices may support for example RESTCONF. 𝐍𝐄𝐓𝐂𝐎𝐍𝐅/𝐑𝐄𝐒𝐓𝐂𝐎𝐍𝐅 - You could manage configuration with NETCONF/RESTCONF directly, but I don't think anyone loves XML or JSON enough to be writing those payloads by hand. 𝐏𝐲𝐭𝐡𝐨𝐧 - You could use Python to manage configurations. Either with a more traditional approach using tools like Netmiko and Scrapli, or the more elegant approach using tools like NAPALM or Nornir. There may be some more development needed with Python, but also total freedom and the ability to extend functionality using a language you have experience with. What tools work best for you? Do you have a mix of tooling?