SHOW ME THE CODE
Why Go Out, We Have TAC at Home
Ever found yourself wishing you had a Cisco TAC engineer on speed dial? While nothing beats the real Technical Assistance Center when you’re deep in production issues, I’ve been playing with a fun little project that brings some of that TAC magic right to your terminal.
Big disclaimer: this is not a real TAC tool, it’s a proof of concept. With that said, don’t go take this out of the lab and use it in production as it’s not ready for that.
The Birth of a Home-Brewed TAC
At its heart, this project is a Go-based CLI tool that combines the power of OpenAI’s GPT-4 with network automation to create an AI-assisted troubleshooting companion. Think of it as your very own junior TAC engineer who never gets tired of running show commands.
How It Works
The magic happens through a surprisingly simple workflow:
- You feed it a question about your network issue
- GPT-4 acts as a network engineer, suggesting safe diagnostic commands
- The tool automatically executes these commands on your device using Scrapligo
- Results are fed back to GPT-4 for analysis
- This cycle continues until GPT-4 either identifies the issue or needs more information
What’s particularly clever about this implementation is its safety-first approach. The system is explicitly instructed to never execute potentially harmful commands - no configuration changes, no debugging commands that might hang the system, and no resource-intensive operations like continuous pings.
The Technical Bits
The code leverages several interesting components:
- Scrapligo: A modern Go library for network device automation
- OpenAI’s GPT-4: Provides the networking expertise and command selection
- Glamour: Adds some fancy terminal rendering for the final explanation
- Context Management: Everything runs with proper context handling for clean operation
The Safety Dance
One of my favorite aspects is the built-in guardrails. The system instruction to GPT-4 is crystal clear:
NEVER return any command that will alter the configuration,
any debug commands, or any other command known to cause issues
When GPT-4 figures out the problem, it signals this with a “VIVACISCO” marker (a silly nod to Cisco), followed by a detailed markdown-formatted explanation of the issue and its resolution.
Future Potential
While this is currently a toy project, the potential applications are intriguing:
- Training Tool: Junior network engineers could use this to learn troubleshooting methodologies
- Automation Framework: The basic structure could be expanded to handle more complex network analysis
- Knowledge Base Builder: Each troubleshooting session could be logged to build a customized knowledge base
- Multi-vendor Support: The code could be extended to support different network vendors and platforms
Limitations and Reality Check
Of course, this isn’t meant to replace real TAC support. It’s more of a “first responder” tool that can help gather initial information and potentially identify common issues. The real value is in its ability to systematically collect relevant information and provide structured analysis. The code is sloppy and has plenty of static items and hardcoded values. There is lots of room for improvement.
Conclusion
While we might not be replacing Cisco TAC anytime soon, this project shows how AI can be practically applied to networking tasks. It’s a perfect example of how we can combine traditional networking tools with modern AI capabilities to create something both useful and educational.
The next time someone asks “Why go out? We have TAC at home,” you can proudly show them this project. It might not be the real TAC, but it’s a pretty fun approximation that actually works!
Remember, in true open-source spirit, this project could be extended in countless ways. Whether you’re learning network troubleshooting or just want a quick way to gather diagnostic information, having an AI-powered TAC assistant at home isn’t such a bad thing after all.