Intelligent Network Troubleshooting
Most “automation” stops at config changes. I take it further: building systems that automate diagnostics and correlation, so problems aren’t just detected — they’re explained. By parsing CLI outputs, syslogs, and SNMP data, I can stitch together the bigger picture automatically.
My Approach
Automated Diagnostics – parsing CLI commands (e.g., interface status, routing adjacencies) and flagging anomalies.
Cross-Data Correlation – linking logs, SNMP traps, and performance counters to identify the true root cause, not just symptoms.
Context-Aware Analysis – recognizing that “link down” + “neighbor loss” + “CPU spike” together point to a different issue than each alone.
Actionable Insights – outputs aren’t raw dumps; they’re structured summaries engineers can act on immediately.
Agentic Tie-In
I connect this troubleshooting layer with my AI/NLP frameworks so the system doesn’t just surface anomalies — it can also:
Interpret results through intent classification (e.g., “what’s wrong with Core01?”).
Suggest next troubleshooting steps automatically, using playbook logic.
Escalate intelligently: if a step fails to resolve, it knows the next diagnostic to run.
Advancing Further
I continue to expand methodology toward:
Event Correlation Engines – rule-based or ML-driven systems for multi-symptom RCA.
Adaptive Playbooks – automated “if this, then run that” flows tied to specific device families.
AI-Augmented Operators – agentic systems that don’t just answer questions, but actively drive troubleshooting workflows.
Why It Matters
Troubleshooting is where downtime costs money. By automating not just the collection of data but also the interpretation — and tying it into an agentic framework — I cut MTTR and deliver engineer-grade insight in real time.
