Overview The March AMER West User Group brought together network engineers, DevOps leaders, and IT operations teams for a highly engaging session centered on automation at scale. The discussion focused on eliminating manual remediation work, reducing SLA penalties, and using LogicMonitor as the foundation for intelligent, API-driven automation. In this session, Debasish Bahinipati shared a real-world automation journey at NICE, walking through how his team evolved from reactive tunnel-outage response to fully automated remediation. By combining LogicMonitor alerts with dynamic Python scripting and API orchestration, the team reduced customer downtime from 20 to 30 minutes to under one minute, without human intervention. The result was measurable impact. Over 4.25 million alerts have been automatically processed and remediated, dramatically improving uptime, operational efficiency, and customer satisfaction. Key Highlights Automation at Scale: Built a custom orchestration layer that reads active alerts via LogicMonitor API and dynamically generates remediation scripts. From 30 Minutes to 60 Seconds: Reduced SLA impacting tunnel outages from manual troubleshooting to near real-time automated resolution. 4.25 Million Alerts Processed: Successfully automated millions of remediation actions, saving significant operational time and penalty costs. Dynamic Script Generation: Used parameters driven by Python templates to generate runtime scripts based on alert metadata. Logging and Safeguards: Implemented full logging, output validation, encrypted variable handling, and retry logic to prevent misfires. Edwin and AIOps Discussion: Community members shared candid insights on early Edwin AI adoption, alert correlation challenges, and practical expectations for AI-assisted operations. Q&A Q: Have you run into any problems using LogicMonitor as the initial trigger for automation at scale? A: “When we went to production scale, we realized it wasn’t sixty seconds anymore. It was taking three or four minutes. We had to optimize our scripts, process from a queue, log unique alert IDs, add retries, and build exclusions to make it near real-time.” Q: Do you log when scripts run and whether they successfully fix the issue? A: “Whenever we run a script, we log everything. We log the alert ID, the full output of the commands, and whether it was successful or not. We even built an analyzer to validate whether the script output matches what we expect.” Q: Are logs and metrics stored natively in LogicMonitor? A: “We are pulling alerts from LogicMonitor through the API, but we built logging and time series tracking behind the scenes. We can see how many alerts we process per day, per hour, even in ten minute intervals.” Q: Have you evaluated Edwin AI for alert correlation and reduction? A: Community feedback highlighted strong out-of-the-box deduplication improvements, but also noted that AI-driven correlation still requires tuning and real-world validation before relying on it for full automation workflows. Customer Call-outs ⭐ “It’s a great presentation. The flexibility of the platform is amazing, especially when you get into scripts.” What’s Next 🎿 LogicMonitor Ski School March 9 – April 5 Our 4-week digital enablement program is designed to accelerate adoption and build confidence with AI-powered observability. Participants will: Fast-track LogicMonitor and Edwin AI skills Join weekly live expert demos and open Q&A Earn 4 badges, including the new Edwin AI badge Apply AIOps best practices in real-world use cases If your team is working toward operationalizing AIOps with structure and governance, this is a strong starting point. ➡️ Join today Logs for Lunch Insight from Logs with Edwin AI March 11 Leverage log data in LM Envision to gain smarter, faster insights with Edwin AI. This session focuses on practical techniques for using logs to support investigations, reduce noise, and accelerate root cause analysis. ➡️ Register here Product Power Hour Performance Without Blind Spots: End-to-End Internet Performance Monitoring March 12 Digital service performance depends on systems outside your direct control, including cloud providers, SaaS platforms, APIs, and third-party infrastructure. This session explores how Catchpoint extends visibility across the full internet stack to help close that visibility gap. ➡️ Register here AI Investigation Enhancements for Faster Resolution March 17 Learn how AI-assisted investigations help teams move from alert to insight faster. See how contextual intelligence reduces time spent manually querying data while services are under pressure. ➡️ Register here
