An operations team at one of the Asia-Pacific’s largest managed service providers (MSPs) was drowning in their own success. Years of investment in monitoring tools and automation had created comprehensive visibility—and comprehensive chaos. Engineers opened dashboards each morning to find thousands of alerts waiting, with critical incidents buried somewhere inside.
The scale of the problem was overwhelming their capacity to respond effectively. As the business grew, meeting SLAs became increasingly difficult, and service quality suffered under the weight of alert fatigue.
The MSP needed a fundamental change in approach. That change came in the form of Edwin AI, an AI agent for ITOps. Implementing this AI-powered incident management product achieved measurable results within weeks. Alert noise dropped by 78%, incident volumes decreased dramatically, and the team shifted from reactive firefighting to strategic problem-solving.
Here’s how they transformed their IT operations.
The MSP implemented Edwin AI, LogicMonitor’s AI agent for ITOps, to process alert streams from their existing observability infrastructure. Edwin AI operates as an intelligence layer between their current tools, ingesting raw alerts from across the technology stack, identifying patterns, eliminating duplicate issues, and surfacing incidents that require human attention.
Instead of engineers manually connecting related events across different systems, Edwin AI performs correlation work automatically and routes consolidated incidents directly into ServiceNow.
The implementation created immediate operational changes:
Engineers now receive incidents with the context needed to begin troubleshooting immediately. Edwin AI eliminated the need to hunt through multiple systems to understand system failures. By converting fragmented alert streams into structured incident workflows, it allows technical teams to apply their expertise to resolution rather than information gathering.
Edwin AI delivered measurable improvements within weeks of implementation, including:
These improvements freed up significant engineering time. The team can now concentrate on high-impact incidents and resolve them more efficiently. With fewer context switches between low-priority alerts, engineers gained capacity for proactive system improvements.
The operational transformation benefited both customers and staff. Service quality improved while engineer burnout decreased. The MSP gained a clearer path toward operational excellence through intelligent incident management.
Edwin AI restructured the MSP’s entire incident management process by converting raw alerts into comprehensive, contextual incidents. Engineers receive complete information packages rather than fragmented data requiring manual assembly.
Each incident now includes:
Engineers work with complete narratives that explain what happened, the business impact, and recommended responses.
ServiceNow evolved from a ticket repository into a comprehensive source of truth. Edwin AI feeds deduplicated and correlated events into the ITSM system, ensuring each ticket contains full context rather than isolated alert fragments.
According to the operations lead: “Edwin AI gives us clarity on what’s actually meaningful. We see the complete picture instead of puzzle pieces.”
This workflow transformation changed how the team approaches incident management, shifting from information gathering to solution implementation.
The MSP’s success with Edwin AI has opened the door to even more ambitious operational improvements. With alert noise under control and workflows streamlined, they’re now exploring how AI can move beyond correlation to autonomous decision-making.
Their roadmap includes agentic AIOps capabilities that will surface instant, context-aware answers pulled from telemetry data, runbooks, and historical incidents. Root cause analysis summaries will be delivered directly in collaboration tools like Slack and Teams, accelerating team decision-making. And Edwin’s GenAI Agent will also provide runbook-based recommendations that combine Edwin’s pattern recognition with the MSP’s own operational expertise.
The long-term vision extends beyond faster incident response to fundamentally different operations. Instead of engineers reacting to system events, AI will handle routine remediation while humans focus on complex problem-solving and strategic improvements. This evolution from reactive to proactive to autonomous operations represents the next phase in IT operations maturity.
Their operations lead frames it simply: “We’ve proven AI can sort the signals from the noise. Now we’re working toward AI that can act on those signals automatically.”
AIOps environments have reached a complexity threshold that challenges traditional management approaches. Hybrid architectures, escalating customer demands, and continuous service expectations create operational loads that strain human capacity.
This MSP’s transformation demonstrates a replicable approach: intelligent alert filtering eliminates noise before it reaches human operators, automated correlation and deduplication prevent redundant work, and engineers gain capacity for strategic initiatives that drive business value.
The operational model shift from reactive alert processing to proactive system management addresses the fundamental scalability challenge facing managed service providers today.
According to their operations lead: “Modern ITOps generates a storm of signals no human team can sift alone. AI lets our people do more with less and still raise the bar on service. It turns complexity into a competitive advantage.”
MSPs operating without AI-powered incident management face mounting pressure as alert volumes continue growing while human capacity remains fixed. Organizations implementing intelligent automation now establish operational advantages that become increasingly valuable over time.
For MSPs evaluating their incident management approach, this transformation offers a clear example of how AI can turn operational complexity from a burden into a competitive advantage.
Twelve months ago, we shipped Edwin AI with a specific hypothesis that AI agents could handle the operational drudgery slowing down ITOps teams.
It was a deliberate bet against the cautious consensus that AI should act only as a copilot, limited to offering suggestions. Most AIOps tools still follow that script. They’re stuck surfacing insights and stop short of action. Edwin was built differently. It was designed to make decisions, correlate events, and execute fixes.
A year later, we know our bet paid off.
Edwin is now running in production across global retailers, financial institutions, managed service providers, and more. The results validate something important about how AI can change ITOps work by eliminating the noise that buries it.
Here’s what Edwin AI accomplished in its first year.
Edwin’s first year delivered results across remarkably diverse environments, each presenting unique operational challenges.
Chemist Warehouse operates more than 600 retail locations around the globe with complex multi-datacenter infrastructure. With Edwin AI Event Intelligence, their ITOps team achieved an 88% reduction in alert noise while maintaining full visibility into critical systems. Engineers shifted from constant, reactive firefighting to strategic infrastructure improvements.
The Capital Group, one of the world’s largest investment management firms, processes over 30,000 alerts monthly across regulated financial systems. Edwin enabled their teams to move from volume-based triage to impact-based operations, focusing resources on business-critical issues while handling routine incidents automatically.
Nexon, managing multi-tenant infrastructure for clients across ANZ, saw a 91% reduction in alert noise and 67% fewer ServiceNow incidents. Edwin’s ability to maintain context across client boundaries while acting autonomously improved SLA performance across their entire client base.
A global retailer supported by Devoteam went from managing 3,000+ incidents monthly to fewer than 400, with correlation models delivering accurate results within the first hour of deployment.
Across all deployments, Edwin delivered consistent operational improvements:
Edwin’s impact was driven by key technical advances that pushed the boundaries of what’s possible in agentic AIOps. Our modular architecture matured rapidly, enabling specialized AI agents to handle correlation, root cause analysis, and remediation—each operating with shared context via a unified infrastructure knowledge graph.
This foundation allowed agents to reason in context, collaborate across workflows, and take targeted action.
Key technical milestones included:
Most significantly, Edwin proved it could deliver value immediately and across many use cases—many teams saw working correlation models within hours of deployment, with full operational benefits appearing within the first week.
Edwin’s first year included strategic partnerships that expanded its operational reach. LogicMonitor’s collaboration with OpenAI brought purpose-built generative AI capabilities directly into the agent framework, enabling clear explanations of complex infrastructure behavior in natural language.
The partnership with Infosys integrated Edwin with AIOps Insights, extending correlation capabilities across multiple data planes and observability stacks without duplicating monitoring logic.
Deep ServiceNow integration evolved beyond simple ticket sync to enable true multi-agent collaboration between Edwin and Now Assist, allowing both systems to contribute to faster triage and more intelligent incident handling.
Edwin’s development throughout the year was driven by feedback from teams running it in production under pressure. Every deployment, support interaction, and correlated alert contributed to system improvements.
New AI Agent capabilities launched in beta included chart and data visualization agents, public knowledge retrieval agents, and guided runbook generation—all responding to specific operational needs identified by customer teams.
ITSM integration improvements delivered better field-level enrichment, more reliable bidirectional sync, and clearer handoff traceability to downstream systems.
The continuous feedback loop between operators, telemetry, and product development shaped Edwin’s evolution toward practical operational value rather than theoretical capability.
Year two is about building on what’s working. Our development priorities focus on expanding proven capabilities rather than experimental features.
The roadmap follows the natural adoption curve many teams experienced in year one: starting with alert correlation and noise reduction, adding root cause analysis and automated workflows, then expanding into predictive operations.
A year ago, we hypothesized that AI agents could handle operational complexity. The evidence is now clear: they can, and teams that deploy them gain significant competitive advantage.
Edwin’s success across diverse environments validates a broader principle about AI in operations. The technology works best when it operates autonomously.
The teams running Edwin today are solving different problems than they were a year ago. They’ve moved beyond alert fatigue into predictive operations, automated remediation, and strategic infrastructure planning.
The technology works. The results are measurable. The transformation is real.
There’s a common misconception in IT operations that mastering DevOps, AIOps, or MLOps means you’re “fully modern.”
But these aren’t checkpoints on a single journey to automation.
DevOps, MLOps, and AIOps solve different problems for different teams—and they operate on different layers of the technology stack. They’re not stages of maturity. They’re parallel areas that sometimes interact, but serve separate needs.
And now, a new frontier is emerging inside IT operations itself: Agentic AIOps.
It’s not another dashboard or a new methodology. It’s a shift from detection to autonomous resolution—freeing teams to move faster, spend less time firefighting, and focus on what actually moves the business forward.
In this article, we’ll break down:
Let’s start by understanding what each “Ops” term means on its own.
Modern IT environments are moving targets. More apps. More data. More users. More cloud. And behind it all is a patchwork of specialized teams working to keep everything running smoothly.
Each “Ops” area—DevOps, MLOps, AIOps, and now agentic AIOps—emerged to solve a specific bottleneck in how systems are built, deployed, managed, and scaled and how different technology professionals interact with them.
Notably, they aren’t layers in a single stack. They aren’t milestones on a maturity curve. They are different approaches, designed for different challenges, with different users in mind.
Understanding what each “Ops” area does—and where they intersect—is essential for anyone running modern IT. Because if you’re managing systems today, odds are you’re already relying on several of them.
And if you’re planning for tomorrow, it’s not about stacking one on top of the other. It’s about weaving them together intelligently, so teams can move faster, solve problems earlier, and spend less time stuck in reactive mode.
Each “Ops” area emerged independently, to solve different challenges at different layers of the modern IT stack. They’re parallel movements in technology—sometimes overlapping, sometimes interacting, but ultimately distinct in purpose, users, and outcomes.
Here’s how they compare at a high level:
| Term | Focus Area | Primary Users | Core Purpose |
| DevOps | Application delivery automation | Developers, DevOps teams | Automate and accelerate code releases |
| MLOps | Machine learning lifecycle management | ML engineers, data scientists | Deploy, monitor, and retrain ML models |
| AIOps | IT operations and incident intelligence | IT Ops teams, SREs | Reduce alert fatigue, detect anomalies, predict outages |
| Agentic AIOps | Autonomous incident response | IT Ops, platform teams | Automate real-time resolution with AI agents |
DevOps is a cultural and technical movement that brings together software development and operations to streamline the process of building, testing, and deploying code. It’s responsible for replacing much of the slow, manual processes involved in automating pipelines for building, testing, and deploying code. Tools like CI/CD, Infrastructure as Code (IaC), and container orchestration became the new standard.
Bringing these functions together led to faster releases, fewer errors, and more reliable deployments.
DevOps is not responsible for running machine learning (ML) workflows or managing IT incidents. Its focus is strictly on delivering application code and infrastructure changes with speed and reliability.
DevOps automates the build-and-release cycle. It reduces errors, accelerates deployments, and helps teams ship with greater confidence and consistency.
As machine learning moved from research labs into enterprise production, teams needed a better way to manage it at scale. That became MLOps.
MLOps applies DevOps-style automation to machine learning workflows. It standardizes how models are trained, validated, deployed, monitored, and retrained. What used to be a one-off, ad hoc process is now governed, repeatable, and production-ready.
MLOps operates in a specialized world. It’s focused on managing the lifecycle of ML models—not the applications they power, not the infrastructure they run on, and not broader IT operations.
MLOps helps data scientists and ML engineers move faster, but it doesn’t replace or directly extend DevOps or AIOps practices.
MLOps ensures machine learning models stay accurate, stable, and useful over time.
AIOps brought artificial intelligence directly into IT operations. It refers to software platforms that apply machine learning and analytics to IT operations data to detect anomalies, reduce alert noise, and accelerate root cause analysis. It helps IT teams manage the growing complexity of modern hybrid and cloud-native environments.
It marked a shift from monitoring everything to understanding what matters.
But even the most advanced AIOps platforms often stop short of action. They surface the problem, but someone still needs to decide what to do next. AIOps reduces the workload, but it doesn’t eliminate it.
AIOps gives IT operations teams a critical edge in managing complexity at scale.
By applying machine learning and advanced analytics to vast streams of telemetry data, it cuts through alert noise, accelerates root cause analysis, and helps teams prioritize what matters most.
Agentic AIOps is the next evolution inside IT operations: moving from insight to action.
These aren’t rule-based scripts or rigid automations. Agentic AIOps uses AI agents that are context-aware, goal-driven, and capable of handling common issues on their own. Think scaling up resources during a traffic spike. Isolating a faulty microservice. Rebalancing workloads to optimize cost.
Agentic AIOps isn’t about replacing IT teams. It’s about removing the repetitive, low-value tasks that drain their time, so they can focus on the work that actually moves the business forward. With Agentic AIOps, teams spend less time reacting and more time architecting, scaling, and innovating. It’s not human vs. machine. It’s humans doing less toil—and more of what they’re uniquely great at.
Agentic AIOps closes the loop between detection and resolution. It can scale resources during a traffic spike, isolate a failing service, or rebalance workloads to cut cloud costs, all without waiting on human input.
Agentic AIOps is not a convergence of DevOps, MLOps, and AIOps. It is a visionary extension of the AIOps category—focused specifically on automating operational outcomes, not software delivery or ML workflows.
Modern IT teams don’t rely on just one “Ops” methodology—and they don’t move through them in a straight line. Each Ops solves a different part of the technology puzzle, for a different set of users, at a different layer of the stack.
They can overlap. They can support each other. But critically, they remain distinct—operating in parallel, not as steps on a single roadmap.
Here’s how they sometimes interact in a real-world environment:
DevOps builds the foundation for fast, reliable application delivery. MLOps adapts some of those automation principles—like CI/CD pipelines and version control—to streamline the machine learning model lifecycle.
They share concepts, but serve different teams: DevOps for software engineers; MLOps for data scientists and ML engineers.
Example:
A fintech company uses DevOps pipelines to deploy new app features daily, while separately running MLOps pipelines to retrain and redeploy their fraud detection models on a weekly cadence.
AIOps ingests operational telemetry from across the IT environment, including systems managed via DevOps practices. It uses pattern recognition and machine learning (often built-in) to detect anomalies, predict issues, and surface root causes.
AIOps platforms typically include their own analytics engines; they don’t require enterprises to run MLOps internally.
Example:
A SaaS provider uses AIOps to monitor cloud infrastructure. It automatically detects service degradations across multiple apps and flags issues for the IT operations team, without depending on MLOps workflows.
Traditional AIOps highlights issues. Agentic AIOps goes further—deploying AI agents to make real-time decisions and take corrective action automatically. It builds directly on operational insights, not DevOps or MLOps pipelines. Agentic AIOps is about enabling true autonomous response inside IT operations.
Example:
A cloud platform experiences a sudden traffic spike. Instead of raising an alert for human review, an AI agent automatically scales up infrastructure, rebalances workloads, and optimizes resource usage—before users notice an issue.
DevOps, MLOps, AIOps, and Agentic AIOps aren’t milestones along a single maturity curve. They’re distinct problem spaces, developed for distinct challenges, by distinct teams.
In modern IT, success isn’t about graduating from one to the next; it’s about weaving the right approaches together intelligently.
Agentic AIOps is the next frontier specifically within IT operations: closing the loop from detection to real-time resolution with autonomous AI agents, freeing human teams to focus where they drive the most value.
Today’s IT environments span legacy infrastructure, multiple cloud platforms, and edge systems—each producing fragmented data, inconsistent signals, and hidden points of failure. This scale brings opportunity, but also operational strain: fragmented visibility, overwhelming alert noise, and slower time to resolution.
With good reason, public and private sector organizations alike are moving beyond basic visibility, demanding hybrid observability that’s context-aware and action-oriented.
To meet this need, LogicMonitor and Infosys have partnered to integrate Edwin AI with Infosys AIOps Insights, part of the Infosys Cobalt suite. The collaboration combines LogicMonitor’s AI-driven hybrid observability platform with Infosys’ strength in automation and enterprise transformation.
“The scale and complexity of today’s enterprise IT environments demand a unified, AI-powered observability platform that can intelligently monitor, analyze, and automate across hybrid infrastructures,” said Michael Tarbet, Global Vice President, MSP & Channel, LogicMonitor. “Our collaboration with Infosys reflects our shared goal of equipping organizations with the tools they need to anticipate problems, reduce downtime, and keep mission-critical systems performing at their peak.”
Enterprises today are operating in environments where no single system tells the whole story.
Critical infrastructure spans data centers, public and private clouds, and edge devices—all generating massive volumes of telemetry. But without a unified lens, teams are left stitching together siloed data, reacting to symptoms instead of solving root issues.
Legacy monitoring tools weren’t built for this level of complexity. What’s needed is an observability platform that both spans these hybrid environments and applies AI to interpret signals in real time, surface what matters, and drive faster, smarter decisions.
This collaboration integrates Edwin AI—the AI agent at the core of LogicMonitor’s hybrid observability platform—directly into Infosys AIOps Insights, part of the Infosys Cobalt cloud ecosystem.
As an agentic AIOps product, Edwin AI processes vast streams of observability data to detect patterns, identify anomalies, and surface root causes with context. And when paired with Infosys’ AIOps capabilities, the result is a smarter, more adaptive layer of IT operations—one that helps teams respond faster, reduce manual effort, and shift from reactive to proactive management.
The power of this partnership lies in the combination of domain depth and technological breadth. LogicMonitor brings its AI-powered, SaaS-based observability platform trusted by enterprises worldwide; Infosys brings deep expertise in enterprise transformation, AI-first consulting, and operational frameworks proven across industries. Together, the two are using IT complexity as a springboard for smarter decisions, faster innovation, and more resilient digital operations.
By integrating Edwin AI with Infosys AIOps Insights, enterprises gain access to a powerful toolset designed to streamline operations and deliver measurable results. Customers can reduce problem diagnosis and resolution time by up to 30%, allowing IT teams to resolve incidents faster and minimize service disruptions. Redundant alerts—a major source of noise and inefficiency—can be cut by 80% or more, freeing teams to focus on what truly requires attention.
Beyond faster triage, the integration provides a unified view across hybrid environments, enriched with persona-based insights tailored to roles, from infrastructure engineers to CIOs. With improved forecasting and signal correlation, IT teams can anticipate issues before they escalate, and make decisions grounded in data, not guesswork.
This collaboration strengthens operational stability and gives teams the insight they need to make timely, confident decisions in complex environments. In an era where digital experience is a competitive edge, the ability to anticipate, adapt, and act with precision is what sets leading organizations apart.
Sally Beauty Holdings offers a clear example of the impact this collaboration can deliver. By leveraging Infosys AIOps Insights, enabled through the LogicMonitor Envision platform, the company increased its proactive issue detection and reduced alert noise by 40%.
This improvement translated directly into minimized downtime and smoother day-to-day operations. The ability to detect and address issues before they escalated meant IT teams could focus on driving value rather than reacting to incidents. Infosys’ commitment to operational excellence played a key role in enabling these outcomes—demonstrating how strategic AI partnerships can drive measurable, business-critical improvements.
The collaboration between LogicMonitor and Infosys brings together two leaders in enterprise IT—pairing deep AI expertise with operational scale. For Infosys, the decision to integrate Edwin AI into its Cobalt suite reflects a strategic commitment to delivering modern, automation-ready observability to its global customer base. For LogicMonitor, it affirms our position as a trusted partner in AI-powered data center transformation, capable of delivering results in some of the most demanding enterprise environments.
“With our combined expertise in AI and automation, we’re redefining the observability landscape,” said Anant Adya, EVP at Infosys. “This partnership represents a significant step forward in enabling enterprises to adopt AI-first strategies that drive agility, resilience, and innovation.”
As organizations push toward more autonomous, resilient IT ecosystems, the LogicMonitor–Infosys relationship sets a new standard for what observability can—and should—deliver.
Most internal AI projects for IT operations next exit pilot. Budgets stretch, priorities shift, key hires fall through, and what started as a strategic initiative turns into a maintenance burden—or worse, shelfware.
Not because the teams lacked vision. But because building a production-grade AI agent is an open-ended commitment. It’s not just model tuning or pipeline orchestration. It’s everything: architecture, integrations, testing frameworks, feedback loops, governance, compliance. And it never stops.
The teams that do manage to launch often find themselves locked into supporting a brittle, aging system while the market moves forward. Agent capabilities improve weekly. New techniques emerge. Vendors with dedicated AI teams ship faster, learn faster, and compound value over time.
Edwin AI, developed by LogicMonitor, reflects that compounding advantage. It’s live in production, integrated with real workflows, and delivering results for our customers. Built as part of a broader agentic AIOps strategy, it’s engineered to reduce alert noise, accelerate resolution, and handle the grunt work that slows teams down.
What follows is a breakdown of what it actually takes to build an AI agent in-house—what gets overlooked, what it costs, and what’s gained when you deploy a product that’s already proven at scale.
Building an AI agent sounds like control. In reality, it’s overhead. What starts as a way to customize quickly becomes a full-stack engineering program. You’re taking on a distributed system with fragile dependencies and fast-moving interfaces.
You’ll need infrastructure to run inference at scale, models that stay relevant, connectors that don’t break, testing frameworks to avoid bad decisions, and enough governance to keep it all stable in production.
None of this is one-and-done. AI systems require constant tuning. As environments shift, so does the data. AI systems degrade fast. Environments shift. Data patterns break. The agent falls out of sync.
Staffing alone breaks the model for most teams. Engineers who’ve built agentic systems at scale are rare and expensive. Hiring them is hard. Retaining them is harder. And once they’re on board, they’ll be tied up supporting internal tooling instead of moving the business forward.
LogicMonitor’s internal data shows that building your own AI agent is roughly three times more expensive than adopting an off-the-shelf product like Edwin AI. The top cost drivers are predictable: high-skill staffing, platform infrastructure, and the integrations needed to stitch the system into your existing environment.
The real cost is your team’s time and focus. Every hour spent maintaining a custom AI agent is an hour not spent improving customer experience, strengthening resilience, or driving innovation. Unless building AI agents is your core business, that effort is misallocated. Time here comes at the expense of higher-impact work.
Buying a mature AI agent allows teams to move faster without taking on the overhead of building and maintaining infrastructure. It removes the need to architect complex systems internally and shifts the focus to applying automation instead of construction.
The cost difference is significant. The majority of build expense comes from compounding investments: engineering headcount, platform maintenance, integration work, retraining cycles, and the ongoing support needed to keep pace with change. These are decisions that create operational weight that grows over time.
Off-the-shelf agents are designed to avoid that drag. They’re built by teams focused entirely on performance, tested in diverse environments, and updated continuously based on feedback at scale. That means less risk, shorter time to impact, and lower total cost of ownership.
The power of agentic AIOps lies in the value it delivers across your organization. Products like Edwin AI aren’t just automating workflows—they’re transforming the way IT teams operate, enabling faster resolution, less noise, and a more resilient digital experience.
At the core of agentic AIOps are four high-impact value drivers:
To go deeper, the reduction in alert and event noise directly translates to avoided IT support costs. With Edwin AI, organizations have reported up to 80% reduction in alert noise, cutting down the number of incidents that reach human teams and freeing up capacity for more strategic work.
Generative AI capabilities—like AI-generated summaries and root cause suggestions—not only improve Mean Time To Resolution (MTTR), they also minimize time spent in war rooms. The result? A 60% drop in MTTR, faster incident triage, and fewer late-night escalations.
By catching issues before they escalate, organizations also reduce the frequency and duration of outages—translating to avoided outage costs and improved service reliability. Fewer incidents means fewer people involved, fewer systems impacted, and happier end users.
Then there’s license and training optimization. By consolidating capabilities within a single AI observability product, companies are seeing reduced licensing overhead and fewer hours spent training teams across disparate tools.
See the data behind the shift to AIOps. See the infographic.
Edwin AI, developed by LogicMonitor, is one example of what an agentic product looks like in production. It’s deployed across enterprise environments today, already delivering outcomes that internal teams often struggle to reach with homegrown tools.
Edwin delivers:
Buying an agentic product like Edwin AI removes the engineering burden, and it gives teams a system that scales with them, adapts to their stack, and starts delivering value on day one. No internal build cycles. No integration firefights. Just function.
Take the Edwin AI product tour.
The benefits of Edwin AI are playing out in real production environments across the globe. Companies in diverse industries are turning to Edwin AI to solve for a number of use cases, including simplifying operations, eliminating noise, and accelerating time to resolution. The results speak for themselves.
These are consistent indicators of how agentic AIOps, delivered through Edwin AI, transforms IT operations.
Building an AI agent for ITOps is a resource-intensive initiative. It requires sustained investment across architecture, infrastructure, staffing, and maintenance—often without a clear timeline to value. Teams that take this path often find themselves maintaining internal systems instead of solving operational problems.
Edwin AI takes that complexity off the table. It’s already in production, already integrated, and already delivering results. Internal analysis shows it’s roughly three times more cost-effective than building from scratch, with real-world returns on investment that include 80% alert noise reduction and a 60% drop in mean time to resolution.
These gains are happening now, in live environments, under real pressure.
For organizations focused on reliability, efficiency, and speed, products like Edwin AI remove friction and deliver impact without adding overhead.
Few teams have the time or capacity to support a product this complex. Most don’t need to. So when budgets are tight and expectations are high, shipping value quickly matters more than owning every component.
Azure environments are growing fast, and so are the challenges of monitoring them at scale. In this blog, part of our Azure Monitoring series, we look at how real ITOps and CloudOps teams are moving beyond Azure Monitor to achieve hybrid visibility, faster troubleshooting, and better business outcomes. These real-life customer stories show what’s possible when observability becomes operational. Want the full picture? Explore the rest of the series.
When you’re managing critical infrastructure in Microsoft Azure, uptime isn’t the finish line. It’s the starting point. The IT teams making the biggest impact today aren’t just “watching” their environments. They’re using observability to drive real business results.
Here’s how a few teams across MSPs, transportation, healthcare, and financial services industries made it happen.
A U.S.-based MSP hit a wall. Their engineers were buried in noisy alerts from Azure Monitor, most of them triggered by hard-coded thresholds that didn’t reflect actual performance risk. Every spike above 80% CPU usage was treated like a fire, regardless of context. Even low-priority virtual machines (VMs) were flooding their queue. The engineers risked missing acting upon critical alerts from customer environments as a result of the alert volume.
They replaced static thresholds with dynamic alerting tuned to each environment’s baseline behavior. They grouped resources by service tier (prod, dev, staging), applied tag-based routing, and built dashboards that surfaced only the issues tied to Service Level Agreements (SLAs). Alert noise dropped by 40%, and engineering teams reclaimed hours every week.
That shift gave them room to grow. With alerting automated and right-sized in hybrid cloud environments, they began onboarding larger Azure and AWS clients, without hiring a single new engineer.
A large State Department of Transportation agency had a major incident stuck open for two years. The issue stemmed from legacy configuration still active on hundreds of network switches scattered across their Azure-connected infrastructure. Locating the problematic issues was manual and perpetually delayed.
They implemented a daily configuration check using LM Envision’s config monitoring module. It parsed more than 200,000 lines of device config and flagged insecure settings, like enablement and out-of-date templates. The checks were centralized and visualized in a dashboard that allowed regional teams to see their exposure and act fast.
The result: the ticket that had been aging for 24 months was resolved in less than 30 days. With better security posture and a clean audit trail, leadership could reallocate engineers on other tasks.
This US-based MSP had strong cloud expertise but couldn’t scale their customer resource onboarding. Setting up Azure, AWS, and GCP monitoring for each new client meant dozens of hours of manual discovery, custom dashboards, and alert tuning. That friction slowed down sales and burned out engineers, risking an impact on customer experiences.
They used LM Envision’s NetScans to automatically detect resources and apply monitoring templates out of the gate. Azure tags were used to dynamically assign devices to dashboards and alert groups based on environment type (e.g., staging, prod, compliance-sensitive).
With onboarding time cut by 50%, engineers stopped spending entire days mapping assets. They started delivering value in hours, not weeks. That faster resource discovery turned into a 25% boost in upsell revenue and stronger relationships with larger clients.
When a major hurricane approached, a national healthcare organization had one goal: to keep patient services online.
Instead of guessing, they built a hurricane dashboard using LM Envision’s map widgets, Azure health metrics, WAN connectivity, and power backup status from key clinics. Each site was tied to alert routing by region and risk level.
When the storm hit, they saw in real time which sites were offline, which were degraded, and which needed immediate intervention. In some cases, IT teams were on-site fixing issues before the clinic staff even realized that the systems were down.
That level of coordination protected patient communication, medication workflows, and internal scheduling during one of the most vulnerable weeks of the year.
This financial services company knew Azure was expensive, but they didn’t realize how many untagged, idle, or misconfigured resources were going unnoticed.
They enabled Cost Optimization in LM Envision, giving them clear visibility into underutilized resources like VMs running 24/7 with minimal CPU usage and premium-priced disks or databases with no active connections. These insights were difficult to surface in Azure Monitor alone.
They also configured cost anomaly detection using custom thresholds tied to monthly budgets. Within 90 days, they identified and decommissioned more than $100K of wasted resources and reduced their mean time to resolution (MTTR) for cost-related incidents by 35%.
Finance teams got cleaner forecasts. Engineering teams kept their performance visibility. Everyone got a win.
Monitoring Azure in a hybrid environment helps MSPs and enterprises deliver more uptime, better security, lower costs, and faster growth. Engineers experience reduced alert noise, lower costs, and faster resource discovery so their time could be better spent solving critical problems related to their businesses.
These teams didn’t just swap tools. They shifted mindsets. If you’re ready to do the same, we’re here to help.
When you’re running IT for 600+ stores across Australia, New Zealand, Ireland, Dubai, and China, “complexity” doesn’t quite cover it. Every POS terminal, warehouse server, store router, and mobile device adds to the operational noise—and when something breaks, the ripple effect hits fast.
For Chemist Warehouse, reliable IT operations is about meeting community healthcare needs, staying compliant across regions, and keeping essential pharmacy services online 24/7.
In this recent LogicMonitor webinar, Cutting Through the Noise, Jesse Cardinale, Infrastructure Lead at Chemist Warehouse, joined LogicMonitor’s Caerl Murray to walk through how they tackled alert fatigue, streamlined incident response, and shifted their IT team from reactive to proactive—with the help of Edwin AI.
If you’re dealing with high alert volumes, fragmented monitoring tools, or the growing pressure to “do more with less,” this recap is for you.
Chemist Warehouse is Australia’s largest pharmacy retailer, but it’s also a global operation. With over 600 stores and a rapidly growing presence in New Zealand, Ireland, Dubai, and China, the company’s IT backbone has to support a 24/7 environment. That includes three major data centers, a cloud footprint spanning multiple providers, and an SD-WAN network connecting edge systems across every store and distribution center.
Jesse Cardinale, who leads infrastructure at Chemist Warehouse, summed it up: “We’re responsible for the back end of everything that keeps the business running 24/7. There’s the technology, of course—but it’s the scale, the people, and the processes can make it a bit of a beast.”
That global scale introduces serious complexity. From legacy systems in remote towns to modern cloud workloads supporting ecommerce, Jesse’s team is constantly balancing uptime, cost control, and compliance across vastly different regions. And unlike a typical retailer, Chemist Warehouse carries an added layer of responsibility: pharmaceutical care.
“Yes, you can walk in and grab vitamins or fragrance,” Jesse said. “But I think the major thing that gets overlooked is that we’re a healthcare provider. We need to be online, operational, and ready to provide pharmaceutical goods to our community.”
This is all to say that IT performance is mission-critical, not a back office function.
Before partnering with LogicMonitor, Chemist Warehouse faced a familiar, but painful, reality: fragmented monitoring tools, endless alerts, and no clear view of what really mattered.
“We had to rely on multiple platforms to get the visibility we needed. What we ended up with was one tool that was specialized in getting network monitoring, another for servers, another for cloud workloads, and so on,” Jesse explained. “Each of these tools had its own learning curve and required dedicated expertise, which made it hard to manage at scale. And the view into our environment was completely siloed. It was difficult to correlate events across the stack.”
That fragmented setup led to alert fatigue. Every system was generating noise. Teams were overloaded, trying to parse out what was real, what was urgent, and what was just background chatter. Without correlation or context, IT spent more time firefighting than preventing issues.
And with thousands of endpoints—POS systems, mobile devices, local servers, and more—spread across urban and remote locations, even a small issue could ripple across the business. A power outage in a single store could trigger ten separate alerts, resulting in duplicate tickets, delayed resolution, and frustrated teams.
The lack of visibility didn’t stay behind the scenes; it disrupted customer experience in real time: “A delay at checkout. A failed online order. A warehouse bottleneck. All of those can impact someone’s ability to get their medication,” Jesse said. “We can’t afford that.”
Compliance made it even more critical. Different countries meant different regulations, and stores needed to stay online and accessible no matter the circumstances. Whether responding to a storm in Australia or managing pharmaceutical access in Dubai, Chemist Warehouse had to deliver seamless, uninterrupted service. No excuses.
To get ahead of the complexity, Chemist Warehouse needed a platform that could centralize their view, eliminate blind spots, and scale with their business. LogicMonitor delivered that foundation.
“We moved to LogicMonitor because it gave us a unified, scalable observability platform,” Jesse said. “Instead of manually defining thresholds or trying to stitch everything together, we could monitor everything and let the AI tell us what actually mattered.”
With LogicMonitor, the Chemist Warehouse infrastructure team consolidated visibility across their global environment—from cloud workloads to edge compute, from distribution centers to retail stores. Integrating with ServiceNow allowed for streamlined incident routing, while dashboards gave teams immediate clarity into system health.
Still, even with this unified observability, the alert volume remained high. Everything was visible, but not everything was actionable.
That’s when the team turned to Edwin AI, LogicMonitor’s AI agent for ITOps.
“It was either spend a lot of man hours trying to further tune the environment, optimize it, work with your teams, or we could get AI to do the work,” Jesse said. “We chose AI.”
Once deployed, the numbers told the story:
Instead of ten noisy alerts, Edwin AI can deliver a single, correlated incident—saving hours of manual triage and helping the right teams respond faster. A power outage at a store, for example, no longer triggered a flood of device-specific alerts. Edwin identified the root cause and pushed a single, actionable incident to ServiceNow.
“The results were dramatic… An 88% reduction in alert volume after enabling Edwin AI. To us, that’s not just a number; it’s a daily quality-of-life improvement for my team,” said Jesse.
With fewer distractions and clearer insights provided by Edwin AI, Jesse’s team was able to shift from reactive incident response to proactive service improvement. Instead of chasing false positives and duplicative tickets, engineers could focus on building new solutions, rolling out infrastructure upgrades, and delivering business value.
“Before Edwin, we needed more people spending more time looking at alerts, just to figure out what was real,” said Jesse. ”Now, we have more time and more people focused on the future.”
That shift enabled faster innovation and more dynamic support for the business. Changes that used to take weeks could now be delivered quickly and with less risk. Incident resolution times dropped, because each ticket came with context, correlation, and actionable insight.
Edwin AI’s integration with ServiceNow played a key role in that improvement. Edwin AI acted as the front door, correlating and filtering alerts before pushing them into Chemist Warehouse’s ITSM workflows. The setup process was simple, requiring just one collaborative session between LogicMonitor and Chemist Warehouse’s internal teams.
“It wasn’t a tool drop. LogicMonitor really partnered with us,” Jesse noted. “Edwin AI made our workflows smarter.”
Fewer disruptions meant more consistent store operations, better customer experiences, and stronger business continuity across regions. The team was also able to support more infrastructure, at greater scale, without increasing headcount.
“We’re actually monitoring more than ever,” Jesse said. “But thanks to Edwin AI, the incident volume went down.”
If there’s one thing Jesse Cardinale emphasized, it’s that observability isn’t something you just “turn on”. It’s a practice that needs attention, iteration, and ownership.
“Don’t treat monitoring like a set-and-forget solution,” he said. “It needs to evolve with your business, just like any other critical platform.”
One of the most impactful decisions Chemist Warehouse made was to dedicate a single engineer to own the monitoring and alerting stack. That person became the internal subject matter expert on LogicMonitor and Edwin AI, working closely with LogicMonitor’s team to fine-tune dashboards, enrich data, and ensure alerts had the right context.
Instead of spending time writing manual thresholds or tuning every alert rule, that engineer focused on feeding Edwin the data it needed to succeed:
This shift, from rules-based monitoring to intelligence-driven signal detection, paid dividends in productivity and clarity.
“The biggest thing that I’ve seen with Edwin AI is that it doesn’t replace our engineers. It complements them. It enables them,” Jesse said. “It gives them the space to think, to solve problems with context and creativity.”
When paired with strong observability practices and intentional data hygiene, AI becomes a force multiplier for your team.
For Chemist Warehouse, the journey with Edwin AI is far from over. The results so far have been impressive, but Jesse and his team see even more potential in continuing to enrich and evolve their agentic AIOps strategy.
The next phase is deeper data integration. The team is working on feeding Edwin AI even more contextual inputs—like incident resolution notes and change data from ServiceNow—to improve the accuracy of root cause suggestions and predictive insights.
They’re also investing in better tagging and metadata hygiene, ensuring that Edwin understands the relationships and criticality of different systems across their hybrid environment.
“Good data in, good data out,” Jesse emphasized. “The more we help Edwin understand our ecosystem, the smarter it gets.”
But through it all, Jesse remains clear-eyed about the role AI should play in IT operations. It’s not magic—and it’s not meant to replace skilled engineers.
That philosophy shapes how Chemist Warehouse continues to scale its infrastructure: by building a symbiotic relationship between people and AI, where each complements the other.
Your systems are getting faster. More complex. More distributed. But your tools are still waiting for something to go wrong before they do anything about it.
That’s the real limitation of most AIOps platforms. They highlight issues. They suggest next steps. But they stop short of action—leaving your team to connect the dots, chase down context, and manually fix what broke.
Agentic AIOps doesn’t wait. It acts.
AI agents detect problems, understand what’s happening, and either fix it—or set the fix in motion. They learn from each incident and carry that knowledge forward. This is infrastructure that can think, respond, and improve in real time.
In this piece, we’ll break down the five core benefit areas of agentic AIOps to show how it helps teams move faster, stay more stable, and scale without the tool sprawl.
Let’s get into it.
Agentic AIOps is a smarter, more scalable way to run IT.
Traditional AIOps helped teams move faster by spotting patterns, detecting anomalies, and speeding up root cause analysis. But under the hood, most of these products still rely on brittle logic—thresholds, static rules, and manual tuning that can’t keep up with constantly changing systems.
When those rules break or environments shift, teams are left scrambling to reconfigure alerts or intervene manually. This all means more noise, slower fixes, and growing maintenance overhead.
Agentic AIOps is a shift from suggestion to action. Instead of surfacing problems and waiting, agentic solutions take the next step: evaluating context, choosing the right response, and executing it autonomously—within the boundaries you set. They learn from every incident and continuously improve.
This doesn’t replace your team; it frees them. No more rule rewrites or repetitive triage. Just faster recovery, smarter operations, and systems that can keep up with change.
Here’s what that enables:
Next, we’ll break down why this shift matters and what agentic AIOps unlocks for modern IT teams.
IT environments aren’t just growing; they’re accelerating. More data, more tools, more systems, more change. Every new microservice, cloud region, or release cycle adds complexity. And while the stakes rise, the number of skilled people available to manage it all? That’s not scaling at the same rate.
Teams today are navigating:
It’s no wonder that IT operations are harder to manage, harder to scale, and increasingly reactive.
Instead of stopping at insight, agentic AIOps closes the loop—moving from detection to autonomous remediation. These agentic systems understand context, evaluate options, and execute the fix. Automatically. In real time. According to the policies and guardrails you set.
This is the foundation for next-generation, self-healing IT operations:
Agentic AIOps gives your IT organization the speed, resilience, and intelligence it needs to keep up with everything else that’s changing.
Struggling to keep up with alerts, triage, and resolution? You’re not alone. Today’s IT teams are expected to resolve incidents faster, with fewer people, across more complex environments. Traditional solutions generate mountains of alerts—but leave the interpretation and response to human operators. That slows things down, increases risk, and pulls engineers away from strategic work.
By embedding intelligent agents that can observe, analyze, and act, agentic AIOps shortens every step of the incident lifecycle. Instead of waiting on manual triage, it detects issues early, understands context, and either recommends or initiates resolution—all in real time.
Here’s how that translates into tangible AIOps benefits:
Agentic AIOps systems are designed to handle the entire resolution loop: from detection to diagnosis to action.
Even when teams know something is wrong, finding why can take hours.
.Legacy monitoring solutions flood teams with alerts—many of them false positives or duplicates.
IT operations often depend on tribal knowledge—what worked last time, and who remembers how it was fixed.
When performance drops, so does trust. Today’s users expect applications and digital services to “just work”—with speed, stability, and no surprises. But maintaining reliability in dynamic, multi-cloud environments is no small task. With constant releases, shifting dependencies, and distributed infrastructure, even small misconfigurations can lead to major disruptions.
Agentic AIOps helps you stay ahead of failure—not just respond to it. By continuously monitoring system health, identifying risks, and taking autonomous action, agentic AIOps prevents downtime and safeguards user experience at scale.
Here are three agentic AIOps benefits that directly improve uptime and reliability:
Modern IT ecosystems are constantly changing—new code, new workloads, new traffic patterns. Static monitoring can’t keep up.
Download our white paper, AIOps Evolved: How Agentic AIOps Transforms IT, and discover how a modular, AI-driven approach can future-proof your operations.
Many high-impact outages start small—subtle memory leaks, creeping latency, or misconfigurations that build up over time.
Some problems don’t show up in a single alert—they show up in patterns over weeks or months.
More complexity doesn’t have to mean more people. As IT environments scale, so do expectations—faster resolution, better uptime, deeper visibility. Growing your infrastructure shouldn’t mean growing your team at the same rate. The real challenge is scaling operations without sacrificing consistency, accountability, or knowledge retention.
By using intelligent agents that learn from context, follow policy-aligned workflows, and capture operational knowledge, agentic AIOps becomes a force multiplier.
Here’s how agentic AIOps helps teams scale smarter and operate more consistently:
Hiring another engineer isn’t always an option; agentic systems can help.
Different teams. Different time zones. Different response styles. Consistency isn’t just about process; it’s about trust in outcomes. Agentic AIOps delivers both.
When knowledge walks out the door, performance suffers.
Documenting after the fact is often the first thing to fall through the cracks.
Training new team members takes time—and access to the right information.
IT budgets are under pressure—but expectations keep rising. Teams are being asked to do more with less: manage larger environments, respond faster to incidents, and support modernization—all without inflating headcount or costs.
Here’s how agentic AIOps helps reduce costs, increase efficiency, and drive ITOps strategy forward:
Cloud spend, licensing, and staffing costs can spiral fast—especially in dynamic environments.
Efficiency is about more than dollars; it’s also about your footprint.
Agentic AIOps is a stepping stone to an entirely new operational model.
Automation without strategy is just efficiency.
Download the EMA report, Unleashing AI-Driven IT Operations, to see how 500+ IT leaders are using AI to accelerate innovation, cut response times, and drive real ROI.
Security threats don’t wait for tickets to be triaged. As infrastructure grows more distributed and dynamic, so do the attack surfaces. At the same time, compliance requirements, incident response times, and audit expectations are tightening. IT teams are caught between the need for speed and the need for control.
By enabling intelligent, real-time response—backed by transparent decision logic and human-defined guardrails—agentic AIOps improves security readiness without sacrificing governance.
Here’s how agentic AIOps supports a more secure, more accountable IT operation:
Modern security incidents evolve quickly. Waiting for manual intervention can cost time, data, and customer trust.
Autonomy doesn’t mean letting go of control. In regulated and high-risk environments, responsible automation is non-negotiable.
Agentic AIOps can transform how IT operations function—but it’s not plug-and-play. To get real value, teams need the right foundation: clean data, defined oversight, and the internal alignment to support responsible autonomy.
First, data quality is non-negotiable. Agentic systems rely on complete, accurate, and timely telemetry—from logs and metrics to traces and event metadata. Without comprehensive observability pipelines in place, AI agents can’t make context-aware decisions, and automation risks becoming noise instead of value.
Next, autonomy still needs oversight. AI agents should operate within clearly defined boundaries, guided by policies that reflect your organization’s tolerance for automation. Teams must define goals, escalation paths, and fail-safes before agents are allowed to take action.
As automation expands, so does the need for governance. Every decision—whether executed or just suggested—should be traceable, auditable, and explainable. This transparency builds trust, supports compliance, and ensures your automation layer remains aligned with broader business objectives.
Finally, your team needs to grow with the system. Agentic AIOps shifts operations from manual response to strategic supervision. That means reskilling teams to configure, monitor, and fine-tune automated workflows—not just react to them. Upskilling isn’t a nice-to-have—it’s what ensures the tech actually gets used.
Agentic AIOps is about giving your people more leverage. With the right foundations in place, teams can trust AI to take on the repetitive work, while they stay focused on what truly moves the business forward.
Today’s IT tools are still stuck reacting—surfacing alerts, surfacing insights. Agentic AIOps changes that by closing the loop between detection and resolution, turning noisy signals into automated action.
This is about fundamentally redesigning how IT operates:
For teams under pressure to do more with less, agentic AIOps offers a path forward. But like any shift, it takes intent. Clean data. Clear policies. And teams ready to lead with oversight—not be buried in alert fatigue.
The promise of agentic AI is operations that can finally keep up with everything else that’s accelerating around them.
On May 1st, AWS corrected a long-standing billing bug tied to Elastic Load Balancer (ELB) data transfers between Availability Zones (AZs) and regions. That fix triggered a noticeable increase in charges for many users, especially for those with high traffic volumes or distributed architectures. The problem wasn’t new usage; it was a silent correction to an old error.
ELBs are designed to distribute traffic across multiple AZs for high availability. For some time, AWS had been under-billing for data transfers across those zones due to a backend miscalculation. Once AWS patched the issue, affected traffic was billed at standard rates.
Here’s what teams started to notice:
Without active monitoring, these increases could’ve gone undetected until the invoice hit.
Organizations using LogicMonitor’s Cost Optimization product quickly saw the impact through the billing widget, which provides real-time visibility into cloud spend across AWS, Azure, and GCP. On May 1st, ELB costs jumped, and LM Envision’s Cost Optimization dashboard in various customer instances could spot the change.
Cost Optimization began showing noticeable increases. In several cases, including in our own LogicMonitor instance, customers reported sudden spikes in previously stable ELB charges, often tied directly to cross-AZ traffic.
This scenario was exactly the kind of scenario the LM Envision platform, paired with Cost Optimization, was designed to catch.
By surfacing changes in real time, whether caused by usage changes, misconfigurations, or (as in this case) vendor-side billing updates, LM Envision gives teams the chance to react before surprises escalate into budget risks..
In an age of dynamic cloud pricing, unexpected billing changes can derail budgets and force last-minute cost corrections. Even minor billing corrections—like this ELB update—can have ripple effects across environments with high traffic or multi-region architectures.
LogicMonitor helps ITOps teams with FinOps responsibilities:
If you’ve seen a sudden spike in your ELB charges this month, take a closer look at:
And if you’re not yet using LogicMonitor to monitor cloud costs and resource changes, now’s the time to see what unified observability can help unlock. When every cloud dollar counts, you need more than reports. You need real-time insight.
When an alert fires, your goal is clear: fix the problem—fast. But traditional troubleshooting rarely makes that easy. You’re immediately thrown into decision mode:
All the while, the clock is ticking. The longer you’re stuck guessing what to do next, the longer your downtime drags on, and the more non-value-added engineering time you burn.
LogicMonitor Logs changes this by automatically correlating your logs with the exact metrics, resources, and alerts that triggered the issue, so you’re not starting from scratch.
You’ll see the logs in context, right where the problem occurred, alongside performance trends and system behavior.
And instead of wading through noise, LM Logs surfaces what stands out: rare anomalies, sudden spikes, and machine-learned patterns that haven’t been seen before. It’s observability with built-in intelligence designed to show you why something happened, not just that it did.
Once you’ve got the right data in front of you, the next step is knowing what to do with it.
Let’s walk through a structured workflow designed to accelerate troubleshooting and improve Mean Time to Resolution (MTTR).
When an alert fires, your first task is to gather context fast. Start at the Overview tab in LogicMonitor to immediately grasp the key facts about what happened:
This overview equips you quickly with critical details, guiding your next troubleshooting steps.
Now it’s time to dig deeper into how this alert fits into your performance history. Use the Graphs tab to visualize what’s going on:
Graphs give visual context but may not fully explain why something occurred—that’s our next step.
Logs often hold the clues to what metrics alone can’t reveal. Scroll to the Log Anomalies section at the bottom of the Graphs tab to investigate further:
Log anomalies frequently uncover hidden or less obvious causes of performance problems, helping you narrow down quickly.
If anomalies are intriguing but you still need more details, dive into the full log data:
Raw logs often contain detailed error messages, stack traces, or specific configuration warnings that give clear indicators of the root cause.
Too many logs to read? LogicMonitor Envision helps by identifying recurring patterns automatically:
Using patterns efficiently cuts through noisy logs, quickly revealing meaningful insights.
If you still need more clarity, LM Logs’ Log Analysis feature surfaces critical log insights instantly, without complex queries or deep log expertise:
Log Analysis transforms traditional troubleshooting, eliminating guesswork and significantly speeding issue identification.
Sometimes your issue may need a deeper investigation across broader logs or historical data:
LogicMonitor’s structured workflow takes you far beyond traditional monitoring, enabling rapid, proactive troubleshooting. By seamlessly combining metrics, events, logs, and traces, LM Logs not only accelerates your response time but also gives your team the ability to understand why problems occur, so you can prevent them altogether.
Embrace this structured approach, and you’ll significantly cut downtime, enhance reliability, and confidently manage your complex environments with greater ease and precision.
