LogicMonitor 2025 Wrapped: Unified Observability Without Blind Spots Recap
Overview LogicMonitor’s 2025 Wrapped webinar closed out the year with a focused look at how unified observability is evolving across the platform. The discussion centered on the role of Catchpoint in closing visibility gaps beyond the firewall, especially during major internet and cloud outages, and how it complements LogicMonitor’s core monitoring capabilities. Product leaders, Garth Fort, CPO, LogicMonitor; Andrew Keating, VP, Product Marketing, LogicMonitor; and Howard Beader, VP, Product Marketing, Catchpoint, addressed integration plans, clarified what to expect short-term versus long-term, and responded live to questions around network path visibility, outage context, and operational workflows. The session reinforced a clear theme: knowing where a problem lives, and when it is not yours, changes everything. Key Highlights ⭐ Clear integration direction: LogicMonitor and Catchpoint will remain distinct in the near term, with plans for unified dashboards, correlated alerts, and RBAC as interoperability deepens in 2026. ⭐ Stronger outage context: Internet performance data helps teams quickly determine whether issues are internal or caused by external providers, reducing noise and unnecessary escalations. ⭐ Sonar-style visualizations resonated: Visual mapping of internet conditions stood out as a powerful addition to existing operational dashboards alongside weather, power, and outage data. ⭐ Edwin AI + Catchpoint is live: Customers can already access Catchpoint insights through Edwin AI, bringing external digital experience intelligence directly into AI-driven workflows today. Q&A Q: Will LogicMonitor and Catchpoint fully merge? A: In the near term, they will remain separate products. Starting in 2026, deeper interoperability will be introduced, including unified dashboards, correlated alerts, and shared RBAC. Q: Will Catchpoint initially be available through Edwin? A: Yes. Catchpoint is currently accessible through Edwin AI, enabling teams to incorporate digital experience data into AI-assisted investigations. Q: Are ISP outage dashboards available today? A: Not yet out of the box. The question highlighted strong demand for broader outage intelligence and context. Q: How will Catchpoint work in restricted or FedRAMP regions? A: This was acknowledged as an important consideration, with planning underway for how these environments will be supported. Customer Call-Outs 🌟 “With Catchpoint we would have had knowledge to just hang on, not our problem, but we are impacted.” 🌟 “That sonar would be a great add to our dashboard for weather, fires, electrical outages, and internet outages.” 🌟 “Graphically, it looked like a quick reskin and a link on the left nav would make it look like part of the main LM product suite pretty quickly.” Additional Resources Want to know more about Catchpoint? Check out the FAQs Slides are attached below Review the recording below ⬇️2025 1H Launch: Level Up Your IT Universe!
IT leaders are facing growing cloud complexity, skyrocketing AI workloads, and relentless operational demands. That’s why we’re introducing powerful updates designed to enhance visibility, accelerate troubleshooting, and drive efficiency at scale. Here’s what we’re unveiling today: Best-in-Class Hybrid Observability – Bringing total visibility and efficiency to AI workloads Accelerated Troubleshooting with Logs – Streamlining root cause analysis with AI-driven insights Optimizing Insights and Resolution with GenAI Agent – Advancing AI-driven automation for faster issue resolution Let’s explore how LogicMonitor is redefining IT efficiency. Expanding Best-in-Class Hybrid Observability AI workloads are growing fast, demanding more power, better scalability, and smarter cost efficiency. Managing hybrid environments across public clouds, private data centers, and edge locations? That’s where Hybrid Observability comes in. With LM Envision’s AI-driven observability platform, you get: Full AI workload visibility by tracking GPU utilization, memory, temperature, and power consumption to prevent performance bottlenecks. Unified infrastructure monitoring across AWS, Azure, GCP, and on-prem environments so you can troubleshoot faster and make informed decisions. Optimized AI model and application performance through continuous monitoring of latency, error rates, and throughput to ensure responsiveness. For enterprises running AI and ML workloads, our latest integrations provide visibility into NVIDIA GPUs—helping ITOps prevent downtime and optimize model performance—and Amazon Q Business, so teams can ensure chatbot responsiveness and service reliability for internal business workflows. With Cost Optimization, you can maximize efficiency while reducing spend. Some examples include: Eliminating wasted cloud resources with a multi-cloud billing dashboard that pinpoints cost-saving opportunities. Leveraging AI-powered recommendations to optimize CPU, memory, and storage usage without impacting performance. Taking real-time action with direct AWS and Azure portal integrations, allowing seamless resource adjustments. With Hybrid Observability and Cost Optimization, you don’t just monitor AI workloads—you optimize them for peak performance and cost savings. Accelerating Troubleshooting with AI-Powered Logs Downtime is costly, and jumping between multiple monitoring tools slows down troubleshooting. That’s where LM Logs comes in to make problem resolution faster, smarter, and more automated than ever. Here’s how it helps: Detecting issues before they escalate with AI-driven anomaly detection, giving IT teams early warnings. Streamline log searches using AI-powered filtering to eliminate the need for complex query syntax. Correlate logs, metrics, and past incidents to gain full context instantly and reduce investigation time. Turn complex log data into clear, human-readable insights in seconds with queryless logs. Whether it’s shortening time-to-insight or reducing the noise that gets in the way, LM Logs helps teams shift from reactive to proactive operations. Optimizing Insights and Resolution with GenAI Agent IT environments are more complex than ever, and traditional AIOps isn’t keeping up. That’s why we built Edwin AI's GenAI Agent —a next-generation AI agent that doesn’t just analyze incidents but takes action. So, what sets the GenAI Agent apart? New enhancements take Edwin AI to the next level by: Pinpointing root causes instantly with AI-driven correlation, eliminating time-consuming manual investigations. Reducing alert noise by 80%, filtering out irrelevant alerts so teams can focus on real issues. Resolving incidents faster with AI-generated recommendations and guided troubleshooting steps. Integrating seamlessly with 3,000+ tools, including Datadog, Splunk, Crowdstrike, and ServiceNow, for complete operational visibility. Immediate Value, Fast Deployment Unlike traditional AI solutions that take months to implement, Edwin AI is up and running in days or weeks. With real-time event intelligence, it starts delivering insights within hours. By automating troubleshooting and reducing alert fatigue, Edwin AI is transforming how IT teams operate. Want to Know More? Join us on April 3rd at 11 am CST to get even more information and live demos from our product experts during our 1H Launch webinar - register today! Readings: Modernizing Data Centers for AI Cost Optimization Observability for AI Workloads Accelerating Troubleshooting with Logs That’s a wrap on our 1H Launch! Until next time—stay proactive, stay optimized, and keep innovating with LogicMonitor!August Product Power Hour: Edwin AI In Action
Overview This month’s Product Power Hour was a deep dive demo experience of LogicMonitor’s Edwin AI, featuring a next-level opportunity to go beyond the fundamentals. We showcased new capabilities, real-world usage patterns, and what’s coming next on the Agentic AI roadmap. The session was packed with live demonstrations, product walkthroughs, and interactive discussions that brought Edwin AI’s intelligent observability features into sharper focus. From alert deduplication to automated investigations and AI-powered root cause suggestions, the session left no doubt about Edwin’s power to reduce noise and accelerate resolution. Attendees gained a clearer understanding of what Edwin AI can do today, as well as what’s possible tomorrow. Key Highlights ⭐ Next-Level AI Investigations: We went beyond the basics to show how Edwin uses out-of-the-box correlation models and enriched context to pinpoint likely root causes faster. ⭐ Targeted Alert Routing: Discussions explored how Edwin AI’s rapid evolution could lead to support role-specific alerting during deduplication events—a capability on the product radar. ⭐ Flexible LLM Usage: The demo showcased how Edwin AI leverages both OpenAI and Anthropic via AWS Bedrock, selecting the optimal model for each task to ensure precision and performance. ⭐ Out-of-the-Box & Tunable Models: Attendees learned they don’t have to start from scratch, asEdwin comes with built-in models that can be adjusted to fit your environment. ⭐ Strong Customer Momentum: The session shared how attendees are actively exploring Edwin AI via Proof of Concepts or preparing for a Q4 rollout. Q&A Q: Are out-of-the-box correlation models available, or do we build from scratch? A: Yes, Edwin AI provides pre-built models that can be fine-tuned for your specific needs. Q: How is customer data handled with LLMs? A: LogicMonitor does not train on customer data. All data is securely segregated, and models are selected based on the task—nothing is shared across tenants. Q: Can we filter out alerts based on custom resource properties? A: Yes, Edwin AI supports filtering logic at ingestion to give you control over what alerts it processes. Q: Can deduplicated alerts be routed to different stakeholders? A: This isn’t available yet, but it’s a hot topic and something we’re exploring for future iterations. Customer Quote Call-outs 🌟“We’re planning a PoV and are really curious to see how Edwin handles topology-driven data.” 🌟“Excited about where this is going—we’d love to see automation and self-remediation layered in.” 🌟“The multi-model approach makes so much sense—great to see task-specific LLMs being used.” What’s Next 🏕️ Camp LogicMonitor: An Observability Adventure On August 18th, we kicked off our first Camp LogicMonitor! Join us for this 4-week virtual learning experience designed for LogicMonitor users of all levels. Each week features self-paced lessons, community discussions, and live Campfire Chats with product experts. Earn badges, grow your skills, and score exclusive LogicMonitor swag! 👉 Register now to reserve your spot! 👥 User Groups Connect in person with other LM users in your city over dinner and real talk. Share wins, swap stories, and grow your network. RSVP today: Denver - September 10 Stay tuned to our LM Community User Group Hub for upcoming virtual sessions. Note: As we finalize our speakers, these dates and times may change, but be sure to register for your respective regions above so we can keep you informed! 🪵 Logs for Lunch September 10: Logs Overages & Reducing MTTR with Cloud Logs ⚡Product Power Hour September 18: Cloud Monitoring With Containers Want to check out previous Product Power Hours? Explore the Product Power Hour Hub in the LM Community! 📚 Badges and Certifications Earn free, on-demand, digital badges that validate your product knowledge and platform skills. Available badges: 🛡️Getting Started 🛡️Collectors 🛡️Logs 🛡️AI Ops Adoption 🛡️Dashboards Review If you missed any part of the session or want to revisit the content, we’ve got you covered: Review the slide deck Want to dive deeper into this session? Watch the recording below ⬇️2025 2H Announcement: Next LogicMonitor Innovations Unveiled
This update is packed with enhancements designed to help you troubleshoot faster, scale smarter, and integrate deeper. If you build, fix, or integrate with LogicMonitor every day, you’ll want to dive into these new capabilities. Expanding Best-in-Class Hybrid Observability LogicMonitor already leads the way in hybrid coverage, and we’re taking it further with Oracle Cloud Infrastructure (OCI) monitoring. Now you can view OCI alongside AWS, Azure, and GCP—all within LM Envision. That means: No more custom scripts. No more blind spots. Seamless, side-by-side visibility across your multi-cloud stack. For engineers, this means easier cloud comparisons and correlation. For your business, it reduces tool sprawl and ensures fewer gaps in observability. Accelerating Troubleshooting Troubleshooting gets a boost with new automation and visualization: Automated Diagnostics Diagnostics like traceroutes, interface stats, and top CPU processes now run automatically the moment an alert fires. Topology Edge Status Color-coded topology maps provide instant visibility into network health. These enhancements cut down mean time to resolution (MTTR), reduce escalations, and strengthen SLA performance. Optimizing Insights & Resolution with Edwin AI This release elevates Edwin AI into a true proactive AI Agent by: Executing remediation workflows through orchestration tools. Leveraging your Knowledge Base for customer-specific context. Integrating directly with IT Service Management tools like ServiceNow, Jira, PagerDuty, and BigPanda. Smarter Event Intelligence Powered by anomaly models and topology-based correlation—makes Edwin AI even more effective. The result? Lower MTTR, reduced burnout, and AI that actually helps when things break. Unlocking Dynamic & Interactive Visualizations If dashboards are your daily workspace, this one’s for you: LMQL-powered widget actions Now, you can drill down, filter, and pivot in real time without rebuilding dashboards. Improved reports Cleaner UX, better summaries, and dynamic formatting. This makes ad-hoc investigations faster and surfaces trends without needing external business intelligence tools. Delivering App Visibility for ITOps We’re introducing three key features that bring application-level visibility to ITOps teams: Dynamic Service Insights Define services with rules and filters, getting instant health status without manual setup. LM Uptime Website checks now appear as first-class resources, complete with dashboards and alerts. Log Metricization Convert log events into metrics you can trend, alert on, and report against. Together, these updates move teams from “Is it broken?” to “What’s the customer impact?” in seconds. 🔔 Coming soon: We’re also rolling out a new badge on Service Insights to make service health and dependencies even more visible at a glance. Stay tuned for details in the Community! Resources & Next Steps To dig deeper into the release, we’ve got you covered: 📢 Read the full announcement blog for all the details. 🌐 Explore our new product pages for in-depth looks at these features Dynamic Service Insights Edwin AI LM Uptime Oracle Cloud Infrastructure Monitoring 🎥 Register for the 2H 2025 Release Webinar to see live demos, ask questions, and get a guided walkthrough of what’s new. These updates are built for the engineers, operators, and teams who keep systems optimized and scalable every day. 👉 Log in to your LM Envision portal to explore the new features. 👉 Join the LogicMonitor Community to share your own use cases and learn from peers. Here’s to smarter observability in 2H 2025.Edwin Before the Action: Best Practices for Ongoing Success
Implementing Edwin AI is a major step forward, but the real momentum begins once it starts analyzing your environment. After go-live, teams often want to know what to focus on first, how to keep Edwin AI calibrated, and which practices help maintain strong outcomes as usage grows. This post highlights the most important areas to prioritize for the period following go-live. These practices help strengthen your environment, build trust in insights, and quickly and sustainably scale Edwin AI’s value. Maintain and Improve Model Performance Correlation models sit at the core of Edwin AI insights. Treating them as dynamic components, not static configurations, is one of the fastest ways to elevate accuracy and reduce noise. Strong early habits: Monitoring the balance of singleton alerts versus correlated insights Adjusting similarity thresholds when alerts are not clustering effectively Cloning and versioning models before making updates Retiring or archiving models that no longer produce meaningful insights These actions create a healthy calibration rhythm that helps Edwin AI stay aligned with real incident patterns. Prioritize Data Quality Clean, consistent metadata is one of the biggest drivers of Edwin AI accuracy. Standardized data enables more reliable correlation, clearer insights, and improved triage. Focus areas for the first few months include: Standardizing key properties like service, owner, application, location, and environment Correcting incomplete or inconsistent metadata (e.g. blank fields from CMDB or other sources and consistent naming conventions) Ensuring new resources follow the established metadata standards Prioritizing data quality early supports more precise insights and reduces manual rework later. Monitor Integrations and Credentials Integrations are essential for keeping Edwin AI in sync with your workflows and other systems. Stable ingestion and correct field mappings support accurate insights and incident creation. Key practices: ✅ Documenting which integrations rely on each API key ✅ Applying least-privilege access for all integration credentials ✅ Reviewing integration field mappings on a regular cadence ✅ Verifying the accuracy of incident creation after updates Staying proactive about integration health ensures Edwin AI continues to work as configured across your tool stack. Track Core Metrics Early and Often Meaningful improvements often appear quickly once Edwin AI is active. Tracking specific metrics helps validate performance gains and ensures the environment continues moving in the right direction. Metrics to track: Noise reduction percentage Insight accuracy compared to incidents Speed of RCA Reductions in manual escalations MTTR trends and comparisons These metrics provide a clear picture of improvement, help teams understand progress, and identify opportunities for additional refinement. Strengthen Rules and Actions Rules and actions turn insights into operational workflows. Once correlation accuracy is validated, refining these configurations can significantly improve efficiency and consistency. Focus on: Reviewing default rules to understand how they work before customizing Validating routing and assignment groups Creating or adjusting actions to match your operational processes Testing rule and action changes in a sandbox environment first Auditing auto-close and auto-resolve behavior Transparent governance around rules and actions helps teams build predictable, low-friction workflows. Improve Cross-Team Collaboration Edwin AI often becomes the connective layer across operations teams. The early months are a great opportunity to reinforce shared workflows and build alignment around how insights are used. Support collaboration by: Holding periodic incident review sessions Investigating issues together within Edwin AI dashboards Standardizing terminology through rules, actions, and metadata Clarifying ownership steps between AI-generated insights and human actions Collaborative reviews help build trust in insights, accelerate adoption, and reduce friction across teams. Use a Continuous Feedback Loop The most effective teams approach Edwin AI as an evolving capability. Continuous iteration ensures the system stays aligned with your environment as it grows and changes. Recommended habits: ✅ Testing model and configuration changes in a sandbox ✅ Exporting and backing up Edwin's configuration before making any changes ✅ Reviewing correlation results weekly or bi-weekly ✅ Logging feedback on insight clarity ✅ Identifying new opportunities for automation ✅ Adjusting similarity thresholds or rules as needed ✅ Periodically reviewing integration and credential health A consistent feedback loop keeps Edwin AI tuned and drives ongoing improvement. Recognize and Celebrate Quick Wins Edwin AI often produces early gains that energize teams and validate the investment. Insightful correlations, rapid noise reduction, and improved troubleshooting are common early outcomes. Highlighting these wins helps maintain momentum and encourages continued engagement from teams across the organization. Final Thoughts The post-implementation phase shapes long-term success with Edwin AI. By focusing on data quality, model calibration, integration health, rules and actions, collaboration, and continuous iteration, teams build a strong foundation for lasting impact. Edwin AI performs best when treated as an evolving, adaptable capability. With consistent tuning and clear operational habits, it becomes a powerful driver of efficiency, resilience, and confident, data-informed operations.Edwin AI Before The Action: Are You Edwin AI Ready?
Imagine your monitoring platform working with you. Root causes surface in minutes, alert noise fades into the background, and repetitive tasks handle themselves safely and automatically. That’s the value Edwin AI brings to observability. But before Edwin AI can deliver that level of impact, your environment must be ready. Technical readiness determines how quickly you’ll see results, while operational and cultural readiness ensure those results stick. This guide helps practitioners and leaders understand their current position and the steps needed to prepare their environment and their teams for real value from Edwin AI. Why Readiness Matters When implemented in a prepared environment, Edwin AI delivers measurable results: Faster Root Cause Analysis (RCA): Root causes identified in minutes, not hours Reduced noise: Up to 70% fewer alerts through event correlation Safe automation: Verified playbooks that act within your control Immediate ROI: Faster time to value and lower operational toil Edwin AI works best when it has a clean, consistent, and connected foundation. Readiness ensures your observability stack can support AI-driven decision-making from day one. The Three Dimensions of Readiness You can think of Edwin AI readiness in three key dimensions. Together, they define whether your observability environment is ready to start seeing value from AI. Operational Readiness & Maturity Goal: Build a healthy, well-structured observability foundation for AI to learn from. Check your environment: ✅ Core monitoring covers infrastructure, applications, and network layers ✅ Metrics, logs, and topology data are connected and visible in LogicMonitor ✅ Alerts use dynamic thresholds and deduplication to minimize noise ✅ Event Intelligence (EI) is active and correlating incidents effectively ✅ Integrations with ITSM or collaboration tools (ServiceNow, Jira, Slack, Teams) are in place If you’re not there yet: Enable Event Intelligence and verify that episodes align with real incidents. Tune correlation accuracy and reduce alert noise before introducing automation. Data and Systems Readiness Goal: Ensure your data is secure, complete, and consistent so Edwin AI can analyze and act confidently. Check your environment: ✅ Data sources (metrics, logs, topology) feed into LogicMonitor without duplication or gaps ✅ Metadata fields like service, application, environment, and owner are standardized ✅ LM Logs are configured with proper tagging to support correlation ✅ Data residency and compliance settings are clearly defined and reviewed ✅ AI permissions and governance policies are documented and understood across teams If you’re not there yet: Focus on cleaning up metadata and validating integrations. Even minor improvements in property alignment can dramatically increase RCA accuracy and correlation reliability. Cultural and Process Readiness Goal: Build team confidence in assistive AI and a clear path to responsible automation. Check your environment: ✅ Incident lifecycle workflows are clearly defined and consistent ✅ Runbooks exist for common issues and follow predictable, step-based formats ✅ Teams know where Edwin AI’s insights will appear (LogicMonitor, ITSM, or chat) ✅ Engineers understand that Edwin AI assists first and automates later ✅ A feedback loop exists for testing and improving AI recommendations If you’re not there yet: Host short internal enablement sessions to show Edwin AI’s assist mode in action. Have engineers validate RCA suggestions and provide real-time feedback. Building trust early lays the foundation for safe and confident automation later. How to Build Readiness Getting ready for Edwin AI doesn’t mean overhauling your entire observability stack. It’s about taking focused, incremental steps that yield immediate improvement. Start with these six actions: Assess your baseline: Measure alert noise, RCA accuracy, and EI correlation rates to understand your current state. Clean up telemetry: Eliminate duplicate alerts, align metadata, and ensure logs and metrics share consistent naming conventions. Activate Event Intelligence: Enable correlation for key services and validate episodes against known incidents to ensure accurate detection and response. Aim for at least 70% correlation accuracy. Train your team: Teach practitioners how to use Edwin AI’s assist mode to analyze RCA and generate insights. Pilot and measure: Start small with a stable service: track alert reduction, RCA speed, and MTTR improvements. Automate safely: Begin with low-risk actions like restarts or notifications. Validate results before scaling to production. Common Readiness Gaps and How to Close Them If you encounter these challenges along the way, here’s how to get back on track: Inconsistent metadata: Run short alignment audits to standardize fields across teams. High alert noise: Enable dynamic thresholds and fine-tune escalation policies. Low correlation accuracy: Adjust cluster density and timeout settings until EI results match real incidents. Unstructured runbooks: Rewrite troubleshooting steps as clear, repeatable actions that can later be automated. Low trust in AI: Keep Edwin AI in assist mode and share RCA examples that match your team’s conclusions to build confidence. Moving from Readiness to Results Readiness isn’t the end goal — it’s the starting point for measurable improvement. Once your environment is stable and connected, Edwin AI begins amplifying what your teams already do best. Each validated RCA helps Edwin AI learn how your systems behave. Each automation that runs successfully builds confidence in safe, explainable AI. Over time, your engineers spend less time triaging and more time improving performance, reliability, and service delivery. To maximize ROI: Start with visible, easy-to-measure services to prove value quickly Quantify improvements in noise reduction and MTTR reduction Keep a feedback loop open to refine models and automation logic Expand automation slowly, backed by results and trust What Success Looks Like When readiness turns into action, success is easy to recognize: Alert noise reduced by 70% or more RCA surfaced in under five minutes Accurate, explainable insights that mirror real incidents Runbooks mapped seamlessly to automation Teams that trust and validate Edwin AI’s recommendations When you reach this point, your organization isn’t just AI-ready, it’s set up to deliver faster, more reliable outcomes at scale.Edwin Before the Action: Getting the Stakeholder Buy-In
From Technology to Transformation AI in operations is no longer an experiment. It is a strategic advantage that helps organizations protect revenue, scale efficiently, and empower their teams. The most successful companies are those that connect technical innovation to the business outcomes their leaders care about. Edwin AI makes that connection clear. It turns observability data into actionable intelligence, automating what slows teams down and amplifying what moves the business forward. The key to unlocking that value is alignment, showing decision makers that Edwin AI directly supports their goals for reliability, efficiency, and growth. Framing the Conversation When you talk to leadership, your role is to bridge technical progress with business priorities. Executives care about three things: impact, risk, and return. Framing Edwin AI in those terms helps the conversation move from "what it does" to "what it delivers". Reliability = Protecting Revenue Edwin AI reduces alert noise and shortens outage response time, ensuring the systems that drive revenue stay online and performant. Every minute saved protects both uptime and customer experience. Productivity = Expanding Capacity Without Headcount Edwin AI automates repetitive triage and root cause analysis (RCA) tasks, freeing engineers to focus on innovation and service improvements. This is measurable productivity growth achieved without additional hiring. Governance = Responsible Automation Executives want AI that can be trusted. Edwin AI’s actions are explainable, auditable, and permissioned, ensuring automation happens only within approved boundaries. Time to Value = Proven ROI Value should be visible early and often. Start small with a focused use case that proves measurable outcomes, such as AI-driven incident triage or automated anomaly detection. Early wins build confidence and set the stage for expansion. How to Speak the Executive Language Your goal is not to explain how Edwin AI works but to show what it makes possible. Focus on value and clarity. Try this framing: “Edwin AI gives us a faster and safer way to protect uptime and reliability. It learns from the data we already collect, helping us identify root causes in minutes instead of hours. The result is fewer incidents, faster recovery, and more time for engineers to focus on high-impact work.” Keep the message outcome-focused by: Replacing technical jargon with language about performance and business continuity. Highlighting how Edwin AI improves reliability and customer experience. Positioning automation as confidence at scale, not replacement. The Metrics That Matter Executives buy results, not roadmaps. Bring proof that connects directly to business value: Over 80% reduction in alert noise, allowing teams to focus on meaningful signals. Accelerated RCA that cuts hours off resolution time. Lower MTTR (Mean Time To Resolve) improves uptime and SLA performance. Headcount-neutral efficiency gains that expand delivery capacity without increasing cost. When you quantify impact in this way, AI becomes a business enabler rather than a technical experiment. The Path to Proof Position the Edwin AI rollout as a structured journey that demonstrates measurable impact while aligning with business objectives. Phase 1: Baseline and Measurement Measure current alert volumes, RCA time, and escalation frequency. Identify three to five services for pilot coverage. Phase 2: Assist and Validate Run Edwin AI in assist mode. Validate its RCA accuracy, noise reduction, and time savings. This stage builds credibility and trust before automation begins. Phase 3: Recommend and Automate Activate low-risk automations for repeatable workflows. Track results against baseline metrics and present the business impact: improved efficiency, faster recovery, and reduced operational cost. Turning Outcomes Into ROI Executives do not buy features; they invest in outcomes that improve the bottom line. Present Edwin AI’s value through the metrics that matter most to them. Cost avoidance: Fewer war rooms, faster RCA, and reduced incident overhead. Productivity gains: Hours reclaimed and reinvested into roadmap delivery. Customer impact: Improved uptime and SLA compliance, leading to stronger retention. “With Edwin AI, we are not just fixing problems faster. We are preventing them, protecting revenue, and creating capacity that lets us grow without increasing cost.” From Buy-In to Belief True adoption happens when leadership sees Edwin AI as a strategic asset, not a tool. The results speak for themselves: reduced noise, faster RCA, improved uptime, and measurable operational ROI. Your job is not to sell AI. It is to show that Edwin AI is already driving the kind of business outcomes your executives care about most: efficiency, resilience, and performance that scales.November Product Power Hour Recap: Exploring Agents with Edwin AI
Overview This month’s Product Power Hour explored the evolution of Edwin AI’s Agent Framework, showing how LogicMonitor is building toward a more autonomous, intelligent observability platform. The session highlighted how agents connect across systems, correlate data, and simplify root cause analysis, all while keeping compliance and control top of mind. Attendees saw a live demo of new agent types, learned how AI-powered insights are driving faster triage, and previewed what’s next for 2026. Key Highlights ⭐ Edwin AI’s Agent Architecture took center stage, showcasing how internal and external agents work together to enrich context, automate responses, and reduce manual triage. ⭐ LMDX extensibility is unlocking new customization opportunities, allowing customers to build and deploy their own internal agents without added licensing. ⭐ Security and compliance remain a top priority, with the team sharing a new FAQ resource that details Edwin AI’s privacy, governance, and data protection standards. ⭐ MSP tenant segmentation has arrived, enabling per-client control and safe, isolated deployment options for managed service providers. ⭐ UI integration is on the horizon, with the roadmap confirming plans to embed Edwin AI directly within the LogicMonitor platform for a unified user experience. Q&A Highlights Q: Which LLM model is under the hood, and what is the available context size? A: Edwin AI’s architecture is LLM-agnostic—it can integrate with different large language models based on use case and fit. Q: When searching for an issue related to a device, can it search logs to help find the root cause? A: Yes, Edwin AI can correlate data across alerts, incidents, and logs to identify likely root causes. Q: How does Edwin AI ensure data privacy and compliance with company policies? A: A comprehensive security FAQ covers data privacy and governance best practices and is available through the product team. Q: As an MSP, how would per-client segregation work? A: Edwin AI supports per-tenant isolation, ensuring each client’s data and automation remain securely separated. Q: Can Edwin AI be deployed for one customer and not all customers under a company tenant? A: Yes, deployment can be selective, allowing MSPs to introduce Edwin AI gradually per client. Q: Can Edwin AI create dashboards or reports for SaaS outages (e.g., Cloudflare, Office365)? A: That functionality is part of upcoming workflow enhancements focused on automated incident and outage reporting. Customer Call-outs ⭐ "[This session was] a great look into how far Edwin AI has come." ⭐ "An exciting step forward for observability." ⭐ MSP participants highlighted that the ability to isolate tenants and deploy selectively was “a game changer.” ⭐ "[The demo] made AI practical and actionable!" What’s Next 👥 User Groups Connect with other LM users in your region, share wins, swap stories, and grow your network. 💻 Virtual User Groups: 🌎 AMER East - Dec 2 at 11am EST 🌐 Register here 🌎 AMER West - Dec 2 at 11am PST 🌐 Register here 🌍 EMEA - Dec 3 at 1pm GMT 🌐 Register here 🌏 APAC - Dec 4 at 10am AEDT 🌐 Register here Note: As we finalize our speakers, these dates and times may change. Be sure to register for your respective region above so we can keep you informed! 🪵Logs for Lunch Proactive Log Monitoring Learn how LogicMonitor’s log analytics help identify anomalies, correlate events, and reduce downtime before incidents occur. 📅 Wednesday, December 10, 2025 🕙 10 AM PT / 12 PM CT / 1 PM ET / 6 PM GMT 👉 Register Now ⚡Next Product Power Hour Accelerated Troubleshooting with Automated Diagnostics Discover how Automated Diagnostics speeds up alert triage using real-time data and AI insights. 📅 Tuesday, December 10, 11 AM CT 👥 Featuring product and training leaders from the Edwin AI team 👉 Register Now 🎁LogicMonitor 2025: Wrapped Celebrate this year’s biggest innovations, milestones, and community moments in our end-of-year showcase. 📅 December 17, 12 PM CT 👉 Register Now Review & Resources If you missed any part of the session or want to revisit the content, we’ve got you covered: Review the slide deck Check out the Edwin Before the Action: Are You Edwin AI Ready? blog Want to dive deeper into this session? Watch the recording below ⬇️November 18 Product Power Hour: Exploring Agents with Edwin AI
Product Power Hour: Exploring Agents with Edwin AI Date: Tuesday, November 18, at 10am CT Register Here Join us for this edition of Product Power Hour, your monthly deep dive into the latest and greatest from LogicMonitor! Hosted by the LM Community, Product, and Training & Enablement teams, this session will spotlight Edwin AI’s new Agent Ecosystem: specialized AI agents designed to accelerate every stage of the incident lifecycle. See how Edwin AI evolves beyond a single agent into a network of specialists—from event intelligence and ITSM, to telemetry, on-call, automation, and more—all orchestrated to take you from alert to resolution faster, with fewer silos and manual bottlenecks. Featuring Guest Speakers: Sarah Luna, Principal Product Manager, AI Max Bradley, Sr Sales Engineer, AI What You’ll Learn: 🤖 Meet the Agents: Discover how event intelligence, ITSM, telemetry, and automation agents each master a critical stage of incident response. 🧠 Orchestration in Action: Learn how the Orchestrator Agent coordinates specialists to triage, diagnose, and remediate seamlessly. ⚡ End-to-End Workflow Demo: Watch Edwin AI resolve a critical alert - from AI summaries and anomaly detection to automated rollback and post-mortem generation. 📈 Best Practices: Practical guidance to pilot agent-assisted incident response in your own environment. 🎬 Live Demo + Q&A: See Edwin AI in action and bring your questions for our product experts. This session is perfect for ITOps, SRE, and platform teams looking to scale operations and reduce MTTR with the power of AI-driven agents.August 19 - Product Power Hour: Edwin AI In Action
Product Power Hour: Edwin AI In Action Date: Tuesday, August 19 at 10 AM CT 🔗 Register Here Join us for August’s edition of Product Power Hour, your monthly deep dive into the latest and greatest from LogicMonitor! Hosted by the LM Community, Product team, and Training & Enablement, this session will focus on Edwin AI, the intelligent assistant in LM Envision designed to help you uncover insights, streamline workflows, and act faster than ever. Featuring Guest Speakers: Sarah Luna, Principal Product Manager Max Bradley, Senior Sales Engineer What You’ll Learn 🤖 Edwin AI in Action: Discover how Edwin AI surfaces insights, streamlines workflows, and enhances daily monitoring tasks. 🛠️ Product Capabilities Deep Dive: Get a firsthand look at the Edwin AI features available today—and what’s coming soon. 📈 User Journey Demo: Watch a live walkthrough of a real-world use case from start to finish using Edwin. 🔮 Preview of What’s Next: Get a sneak peek at Edwin’s upcoming live agent capabilities launching this fall. ❓ Expert Q&A: Bring your questions for our product experts—we’ll leave time to help you maximize Edwin’s potential.
