What Happens After BGP?
Gaining Full Path Visibility in Hybrid Cloud Networks In the modern enterprise, Border Gateway Protocol (BGP) plays a foundational role in connecting distributed networks. It is the routing protocol that...
Every network is unique, facing its own set of challenges and requiring tailored solutions. This is where AI-driven diagnosis automation can make a significant difference. By leveraging the power of AI, organizations can build intelligent and efficient automation workflows that address their specific network needs.
Understanding Your Unique Network: The Foundation for Effective Automation
To build effective automation, it’s essential to understand your network’s unique characteristics. This involves:
- Analyzing historical data: Review past troubleshooting tickets to identify common issues and patterns.
- Leveraging network knowledge: Consider the technologies and configurations used in your network to determine the most relevant automation strategies.
- Harnessing human expertise: Draw on your team’s experience and knowledge of network troubleshooting to inform automation development.
Building Automation with NetBrain’s AI-Powered Platform
NetBrain’s Next-Gen platform simplifies the process of building custom automation workflows, even for those without extensive coding experience. Here’s how it works:
- Decode network features: Use NetBrain’s Golden Engineering Studio’s Reverse Engineering to decode your network’s key configuration and state design rules from millions of lines of configuration.
- Forward Engineering: Pick representative devices and capture troubleshooting ideas as seed intents.
- Replicate and refine: Replicate seed intents across your network to see how many devices have similar issues.
- Leverage AI and human expertise: Combine your team’s knowledge with AI-powered capabilities to generate Action Plans, or guidebooks, for troubleshooting complex steps automatically.
A Practical Example: Troubleshooting Interface Errors
To illustrate this process, let’s consider a common network issue: increasing interface errors.
- Identify the problem: Analyze historical data and observe the frequency of interface error reports.
- Pick a representative device: Capture your troubleshooting idea for interface errors in a seed intent.
- Replicate: Apply the seed intent to all relevant devices in your network to identify similar issues.
- Build an Action Plan: Use NetBrain’s AI-assisted capabilities to create a step-by-step Action Plan for troubleshooting interface errors in the built-in AI Co-Pilot bot.
Conclusion
By following these steps and leveraging NetBrain’s AI-powered platform, organizations can build effective and tailored automation workflows to simplify everyday troubleshooting and improve network performance. By automating routine tasks, IT teams can focus on more strategic initiatives and drive business value.