Skylar AI - The Future of AIOps and Observability
As continued from Part1 of this blog ScienceLogic’s flagship AIOps product, has arguably the industry’s most complete set of integrations for telemetry collection, enabling insights and rapid automation, and supports almost any kind of device and environment - from legacy through to containers and cloud platforms. Skylar AI is designed to work alongside SL1 to deliver a new class of insights, recommendations, visualizations and user experience. At a high-level, the engine behind Skylar AI receives telemetry from SL1 and sends insights back to SL1 that can drive automations, third-party integrations and leverage all other capabilities of SL1. It also has future support to ingest third-party streaming telemetry and customer documents (such as KB articles and support tickets). The data lake within Skylar AI can also be accessed via third-party tools that support ODBC. Skylar AI is Being Released as a Suite of Services Skylar AI will initially comprise three primary services which are briefly described below. 1. Skylar Automated RCA - AI-based Root Cause Analysis for Logs Solves the pain point of identifying and troubleshooting problems found in application and infrastructure logs. Streaming logs are analyzed in real-time to quickly identify details of problems and summarize their root cause showing key log lines and using GenAI to create plain language summaries. Skylar Automated RCA is available now. 2. Skylar Analytics – A set of AI/ML and advanced analytic and data exploration capabilities Skylar Analytics is designed to derive new value from data collected by SL1 by offering: Predictive Alerting – Accurately models and predicts impending issues with key resources such as disks, memory and CPU. Predictive alerting is designed to let you know about potential future issues with enough time to act and prevent them from happening in the first place. Always on Anomaly Detection – When troubleshooting a problem, allows you to see any associated metrics that appear to be abnormal. There is also a capability to set up alerting when a particular metric is anomalous. Data Visualization – A modern and highly flexible dashboarding environment and no-code visualization builder with over 40 visualization types. Data Exploration – The ability to use third-party reporting and data exploration tools that support the industry standard ODBC interface. Skylar Analytics is planned for release in the fall. 3. Skylar Advisor – Automated guidance to optimize IT operations and proactively avoid issues Skylar Advisor introduces a fundamentally new user experience for the IT Operator. It will automatically provide the user with important insights, predictions and actionable recommendations to keep everything running optimally. Details on availability of Skylar Advisor are coming soon. The latest information about Skylar AI can be found by visiting: https://sciencelogic.com/platform/skylar-aihttps://sciencelogic.com/platform/skylar-ai.Skylar AI - The Future of AIOps and Observability
Everything’s down. Frustrated customers are calling, emailing and posting on social media. You assemble a war room. News of the problem is spreading like wildfire and has just reached the CEO at the most inopportune moment (think dinner/vacation/out with the kids). Your cell phone and Slack feed are going nuts. Meanwhile, the front-line technical teams are looking at dashboards and digging through logs, events and metrics, trying to understand what happened. They’re not making much progress, so you escalate and call in more experts. An hour later there’s still no obvious answer. You escalate it again, but this time to the development team. And, eventually, 14 very long hours later, a “rockstar” developer figures it out. The immediate crisis is over, but there will be painful days of work ahead for you and your team dealing with disgruntled customers, driving a detailed postmortem process and then coming up with an action plan to prevent a repeat occurrence of what just happened. Enter Skylar AI - The Future of AIOps and Observability Now imagine a different scenario: Skylar: “Your orders server just went down because it hit a race condition in one of the open source components. Good news, there’s a quick solution. You are running v2.3.1.2 of the component and the issue has been fixed in v2.3.1.4. Would you like me to show you how to upgrade to the fixed version?” The goal of Skylar AI is to reason over not just telemetry, but also the stored knowledge of an organization to deliver accurate insights, recommendations, and predictions, so that: When something fails, it will tell you in plain language what happened and how to fix it. If something is going to fail, it will tell you how to prevent it from failing and impacting production. It will also be able to deliver all insights and answer any question asked of it by drawing on telemetry together with relevant information from a company’s stored knowledge (e.g. KB articles, support tickets, bug databases, product documentation, etc.) Gen AI is Everywhere, how is Skylar AI Different? The Fundamental Challenge Before explaining how Skylar AI is different, it’s important to understand the problem Skylar AI was designed to address: that today’s best-of-breed AIOps, monitoring and observability tools place too much reliance on human expertise. The situation above is a prime example. The problem had never been seen before (an “unknown/unknown”) and so there were no rules in place to catch it. This meant human experts with the right tribal knowledge were needed to figure things out. The tools provided all the information needed to ultimately solve the problem, but at each step of the way, the right human expert(s) needed to interpret and analyze the data, decide on the next course of action and keep iterating until the problem was finally solved. Ultimately, this required multiple escalations and a lot of wasted time and frustration. The goal of Skylar AI is to make Level 1 and 2 teams more effective and productive and able to solve a broader range of problems more quickly. So that situations like the above can be handled without the pain and wasted time. So why not Follow the Industry Trend and Build an AI Assistant? The industry is quickly moving towards the use of Generative AI (GenAI) and large language models (LLMs) in the form of “AI Assistants” or “AI Chatbots”. These appear as small chat panels in a product’s UI and allow a user to ask questions of the tool in plain language. So, instead of learning how to construct a complex query and visualize it on a chart, with an assistant, you can simply ask, “create a dashboard showing average latency for the top 10 devices over the last 3 months”, and it will do just that. However, although assistants are useful in simplifying the way a user interacts with a tool, it is important to understand that a skilled user still needs to know what questions to ask of the assistant at each step of the way – in other words reliance on deep expertise is still required to get the most out of the tool. Our Breakthrough Invention: An AI Advisor, rather than an AI Assistant To deliver on our vision, a new paradigm was needed: an “AI Advisor.” The concept of Skylar Advisor is that instead of a skilled user having to always know what questions to ask of the tool, the Advisor automatically tells the user the answers to the curated questions tailored to the user role without the user having to even ask them in the first place! In other words, Skylar Advisor automatically tells the user what the user needs to know in the form of easy-to-understand insights, predictions, and actions to take. This allows teams of all levels to be far more effective, without wasting precious time and perform more tasks with less effort. The creation of Skylar Advisor, didn’t just necessitate building a pipeline of AI and machine learning (ML) technologies including a self-hosted LLM, — it also required a completely reimagined user experience: An uncluttered and intuitive UI (not a traditional dashboard/event list display) A curated list of what’s most important to user based on the user’s role and areas of responsibility A multi-modal interface to describe a problem The use of contextual “Quick Prompts,” where Skylar suggests what actions a skilled expert would take at each step of the way To see a demonstration of Skylar Advisor, please visit here. Make sure to read Part 2 of this blog that explains the components of Skylar AI and how it works with SL1Introducing our newest Blogger: Gavin Cohen VP of AI Product Management
Hello Nexus Community Members and ScienceLogic Customers, I wanted to take a minute to introduce our newest blogger who will be keeping us up to date on AI Product related announcements, trends and best practices. Gavin Cohen is VP of AI Product Management at ScienceLogic and has over 20 years’ experience across a diverse range of technology roles. At ScienceLogic, he is responsible for defining the company’s AI/ML product roadmap and strategy, working closely with customers, partners and internal teams. Gavin joined ScienceLogic through the acquisition of Zebrium, where he was VP of Product and Marketing and part of the founding team. Prior to joining Zebrium, he was VP of Product and Solutions Marketing at Nimble Storage where he redefined the company’s category and positioning leading to a successful acquisition by HPE. He has also held senior product management, business development and technical evangelist roles in Australia and the U.S. Gavin has a Bachelor of Computer Science and an MBA.Skylar One Juneau: More Speed. Better Maps. Smarter Topology. Happier Weekends.
When customers help guide the roadmap, the result looks a lot like Juneau (12.5.1). It’s practical, focused, and full of improvements you asked for. And while this version introduces new marquee features, Skylar One (SL1) customers are more likely to notice how it brings together hundreds of customer-driven engineering upgrades. The result is something admins, operators, and SREs will recognize instantly: a platform that runs faster, models the world more accurately, and fits easily into the rhythm of how you operate. With 700+ enhancements across ingestion, topology, eventing, collectors, HA, UX, and hardware support, Juneau is one of the most customer-driven releases we’ve delivered. These changes come straight from real deployments, break/fix pain points in the field, and hands-on sessions with SEs, Support, and Professional Services. And of course, huge thanks to the brilliant members of the Nexus User Community and all your contributions to the Nexus Idea Hub. Let’s dive into a few highlights from Juneau. A Platform That Feels Faster (Because It Is) Juneau’s performance improvements aren’t theoretical. The ingestion and data processing pipelines now move up to 60% more throughput, thanks to extensive tuning and backend re-architecture. In large environments, this directly translates into smoother dashboards, more up-to-date metrics, and fewer ingestion bottlenecks under unusually heavy load. Daily data maintenance was also rebuilt to use far less CPU and I/O, eliminating one of the most common sources of delay during maintenance windows. For large deployments, this means more predictable performance and smother scaling to peak load. In short: the system breathes easier. And as you already know, when goto ops tools run faster, the entire operational experience improves. Topology That Matches the Networks You Run Topology modeling has always been one of SL1’s superpowers and Juneau gives it a serious upgrade. LLDP and CDP relationship extraction got a ground-up refresh. One of the most impactful changes: LLDP now forms multiple valid relationships across interface pairs instead of aggregating them into one. Bonded links, trunk groups, and redundant uplinks now show up exactly as they exist in the environment. Similarly, topology can now form relationships even when only one side of the connection reports neighbors. For environments with asymmetric discovery policies, strict security controls, or devices that don’t speak every discovery protocol, this is a major practical improvement. You now get a more complete graph, even when the inputs are less than perfect. Add in new global controls for per-protocol behavior, and discovery becomes far more adaptable to the real world — not just an ideal one. Geographic Maps: A New Operational Lens Juneau introduces Geographic Maps as a new data & visualization type, and a new dimension to operational awareness. You can now visualize devices, services, and health states geographically across regions, campuses, data centers, cloud footprints, retail branches, or industrial sites. Here are just a few customer use cases: MSPs validating customer regions Retailers preventing brick-and-mortar outages Energy and utilities tracking state-level impact Distributed enterprises troubleshooting local vs. regional issues And many more… Skylar One Geographic Maps isn’t simply NOC bling — it’s a diagnostic tool. Geographic Maps surface real-world patterns that don’t always fit neatly into lists or dashboards. They make regional correlation simple. And even better, they turbocharge understanding for business services and synthetics. We can’t wait to see how you’ll take advantage of geo-aware data and visualization. And yes, automatically zooming, context-aware, full-screen Global Ops maps do make for sparkly NOC bling. Event Policy Editor: Redesigned by You Juneau includes a new Event Policy Editor that streamlines the configuration and optimization for event policies based on your feedback. The new UI is cleaner with an optimized layout and validates changes in real time. It’s easier to ensure accuracy, faster to build policies, and easy for new admins to learn. Business Service policy tuning also benefits from the same treatment, with clearer rule logic and intuitive metric selection. If you manage complex event pipelines or regularly onboard new operators, this is a quality-of-life upgrade you’ll notice immediately. AI Observability from Model to Metal (Now with AMD GPUs) Believe it or not, self-managed AI infrastructure isn’t niche anymore — it’s becoming core to how advanced operations teams ensure tight AI security and cut cloud opex. Juneau expands Skylar One’s existing NVIDIA GPU and LLM workload monitoring to include full AMD GPU visibility, completing true model-to-metal observability. You get insight into GPU temperature, utilization, memory pressure, power draw, error states, and other AMD-specific signals, all stitched into the same service context that already connects your models, inference endpoints, OS behavior, and chassis health. For teams managing AI platforms, this is welcome news. For example, you can now measure the relative efficiency of specific AI workloads across different combinations of datacenter components and recommend the most cost-effective mix to maximize LLM performance for your operations. If you’re running LLM inference nodes, GPU training clusters, HPC pipelines, or any data-intensive workloads, this enhancement further extends the ScienceLogic AI Platform’s comprehensive observability. Skylar One now interprets the entire AI stack as a single, coherent system — from model behavior to GPU thermals to the infrastructure beneath it. It’s the same level of intelligent, correlated insight you expect for CPUs, networks, containers, storage, cloud, and more, now fluent in even more of the most performance-sensitive workloads you operate. Synthetics Become a First-Class Citizen in Juneau For a long time, synthetic tests in Skylar One were powerful but lived a little off to the side. They were easy enough to integrate but not fully woven into workflow. With Juneau, synthetics become a first-class citizen in Skylar One. Recording a synthetic transaction now feels familiar to anyone who’s used a modern DEM tool: record an application workflow in a browser via Playwright Codegen, drop the generated Playwright script into a Skylar One dynamic app, assign a credential, and you’re ready to test from multiple locations. Just record, point, click, run. But the real shift happens after you deploy synthetic tests. Because they’re now fully integrated into Skylar One, real-world application performance shows up everywhere operators already live — dashboards, business services, context panels, service health, and even Geographic Maps. Multi-location failures become much easier to troubleshoot when you have quantifiable experience data from an end-user perspective. And yes: Skylar One still works seamlessly with your existing DEM, RUM, and APM investments. If you rely on browser-based monitoring from Dynatrace, New Relic, AppDynamics, or others, Skylar One can continuously ingest and contextualize those signals too. But with the Juneau release, external tools are no longer required. Synthetic visibility is built in and part of the core platform experience. For teams who depend on predictable user journeys, distributed site uptime, or multi-step workflows, synthetics in Juneau aren’t just easier to run. They’re now part of the operational heartbeat of Skylar One. High Availability, Low Maintenance Some improvements don’t need a spotlight — they just work the way you expect. Improvements to High Availability is a perfect example. If you’ve ever stared at any application during failover and quietly wondered, “Is it switching… or just thinking about it?”, you’ll appreciate Juneau’s enhancements to Skylar One’s High Availability. This release tightens the entire HA failover path. Link-state detection is faster and more accurate (especially in environments without crossover links). Heartbeat monitoring is more responsive. Cluster state transitions settle quickly. And the updated logic avoids unnecessary retries that slowed down election decisions in some configurations. The result is HA that kicks in quickly, transitions reliably, and behaves with the no-fuss confidence you expect from tier-1 enterprise platforms. Python 3 Everywhere: Faster, Safer, and Ready for What’s Next The Juneau release completes Skylar One’s transition to a pure Python 3 platform. Collectors, platform services, dynamic apps, extensions — everything now runs on a single, modern runtime with no legacy paths or dual execution. Python 3 is industry standard for good reasons. It’s faster, more secure, and far better supported by modern libraries and tooling. Moving to a single runtime improves workload performance, strengthens security posture across the platform, and simplifies everything from dynamic app development to upgrades and long-term maintenance. Deprecated Python 2 powerpacks are clearly indicated in the admin UI so teams can identify and update any remaining artifacts with ease. Skylar One Juneau — Ready for You, Ready for Anything Juneau brings meaningful improvements across the features of Skylar One that operators depend on most. Faster ingestion, sharper topology, integrated synthetics, Geographic Maps, streamlined HA, full-stack AI observability, and a fully modern Python foundation come together in a release that’s more capable, more responsive, and more aligned with how your team works. It was engineered with customer input at its core, RC tested in user environments, and ready to support the next wave of your AIOps workflows. Check out the Getting Started Guide for more details and documentation links. We can’t wait to get your feedback on the latest release from ScienceLogic.
