Mission: AI possible—What agentic AI means for the future of ITOps

If 2023 was the year AI entered the enterprise conversation and 2024 was the year of AI overhype, 2025 is the year it takes action. 

“Agentic AI” has quickly become the banner term for next-gen systems that aren’t limited to generating responses—they operate, decide, and resolve. The shift from passive chatbots to autonomous agents is underway, and for IT operations teams, the implications are massive.

In our recent executive briefing, Decoding Agentic AI for ITOps Excellence, LogicMonitor brought together a powerhouse lineup: Jason Lee, Head of Digital Natives at OpenAI; Karthik SJ, General Manager, AI, at LogicMonitor; and Gaël Grootaert, Group Director Devoteam Managed Services, one of Europe’s leading Managed Service Providers (MSP). This formidable group came together to unpack what agentic AI really means—and more importantly, how it’s already reshaping the way enterprises and service providers manage complex, high-stakes infrastructure.

From reducing 3,000 monthly incidents to under 400, to eliminating overnight escalations by 60%, the conversation spotlighted how AI agents are delivering real operational impact today. Whether you’re deep in ITOps, exploring AIOps, or rethinking how your teams scale, here’s what you need to know from the frontlines.

Breaking down agentic AI

AI agents are quickly becoming the next major leap in how we interact with—and delegate work to—machines. But as Jason Lee, Head of Digital Natives at OpenAI, explained during the webinar, not all “agents” are created equal. The term is everywhere, but few are defining it with precision. At OpenAI, they’ve drawn a clear line in the sand: agents aren’t just conversational. They’re operational.

Unlike traditional LLM-powered chatbots that respond to prompts, AI agents are systems that take action on your behalf. They follow goals, execute workflows, interact with tools, and—critically—operate within guardrails. According to Jason, OpenAI sees 2025 as “the year of agents”, where we move beyond novelty into true utility.

OpenAI defines agentic AI as AI that is capable of doing work for you independently—you give it a task and it will execute it. Jason shares typically, there are four essential components:

1. Intelligence: The LLM provides the foundational reasoning capability.
2. Workflow: A structure or routine that guides the agent’s objectives and steps.
3. Integrations: APIs or external systems the agent can call on to complete tasks.
4. Guardrails: Controls that ensure reliability, security, and alignment with intended outcomes.
   

But the real breakthrough isn’t in building smarter individual agents—it’s in orchestrating networks of specialized agents that can collaborate, hand off tasks, and solve more complex problems. Think of it as nodes in a system that continuously learn, iterate, and scale together.

This architecture is already delivering real results:

• Customer support: At OpenAI, many support tickets are now resolved by agents—some even multimodal, handling voice and text.
• Employee productivity: Coding assistants are now capable of generating meaningful code autonomously, including one example where an organization rolled out a system that now does the work of roughly a thousand.
• Business decisioning: Agents are augmenting teams by retrieving, analyzing, and contextualizing data at a speed and scale humans simply can’t match.
  

As Jason put it, we’re witnessing a shift from “AI as a search engine” to AI as a workforce extension—automating the mundane, accelerating the complex, and redefining what scale looks like inside the enterprise.

Why the AI data center boom matters

If agentic AI is the engine, infrastructure is the fuel—and OpenAI is betting big on both. Jason Lee pulled back the curtain on The Stargate Project, OpenAI’s $500 billion moonshot investment in data infrastructure. This project’s goal is to build new AI infrastructure for OpenAI in the United States. Jason shared that this is a core part of how OpenAI will continue to build out its capabilities, which will lay the groundwork for a future where AI agents are everywhere, embedded in workflows, running autonomously, and available to everyone, all the time.

Why the urgency? Because demand is exploding. AI agents are no longer only processing text; they’re interacting via voice, analyzing images, generating video, and making decisions in real time. That level of capability requires serious compute. According to Jason, the next few years will bring a near-insatiable appetite for processing power, as more businesses and individuals lean on AI to handle daily tasks, operational workflows, and even strategic planning.

But the picture isn’t one-dimensional. As models get smarter, they’re also becoming more efficient—able to do more with less. The tension between increasing demand and improving efficiency has led to two competing theories:

1. Massive-scale infrastructure is essential to keep up with future demand for intelligent agents across personal and enterprise contexts.
2. Model efficiency will outpace demand, making AI broadly accessible without needing hyperscale infrastructure.

For IT operations teams, the message is clear: you can’t afford to scale infrastructure reactively. You need intelligent, observability-driven, AI-augmented systems that evolve as fast as the workloads they support. Whether it’s triaging bugs, correlating alerts, or managing hybrid environments, the infrastructure that supports AI must now be as agile and adaptive as the AI itself.

Agentic AIOps at LogicMonitor 

Despite years of hype, traditional AIOps has largely underdelivered. It’s been too reactive, too brittle, and too focused on dashboards over outcomes. For organizations buried in alerts and complexity, legacy AIOps tools have become another signal in the noise.

Instead, LogicMonitor’s approach is to be agentic-first and outcome-focused, built not to surface insights, but to take action. At the heart of that strategy is Edwin AI, an AI agent for IT operations.

Edwin is not a chatbot. It’s not a copilot. It’s an operational partner that works across your entire IT ecosystem—on-prem and in the cloud—to scale your team’s ability to detect, diagnose, and resolve issues in real time.

LogicMonitor’s platform processes over 1.5 trillion metrics every day across hybrid infrastructures. That’s observability at planetary scale—fueled by a combination of data depth, automation, and generative AI. Here’s how Edwin AI turns that volume into velocity:

• Event Intelligence: Edwin cuts through alert fatigue by correlating noisy signals, identifying root causes, and surfacing what actually matters.
• GenAI Agents: Need to know what changed in the last 48 hours? Build a dashboard on the fly? Pull documentation from a vendor site? Edwin does it instantly—across systems, data sources, and formats.
• Automation: Edwin can execute runbooks, trigger workflows, and support self-healing, without human intervention.

As a result, Edwin gives teams time back, shrinks MTTR, and lets skilled engineers focus on strategy instead of firefighting.

Partner spotlight: How Devoteam is scaling smarter with agentic AI

With over 11,000 consultants across 25+ countries, Devoteam is one of Europe’s top-tier managed services providers—and a leader in driving digital transformation through cloud, cyber, data, and sustainability. As a triple-certified partner across AWS, Google Cloud and Microsoft Azure, the company has built its reputation on navigating technological complexity while delivering measurable impact.

Why AI agents matter for MSPs

As Gaël Grootaert, Group Director at Devoteam Managed Services, put it: “We’re firefighters sometimes… AI helps us mitigate everything that has an impact on the customer side.”

For modern MSPs, customer expectations are rising while margins for error are shrinking. AI agents have emerged as a way to unlock both scale and speed, empowering front-line engineers and back-end support teams to work faster, smarter, and with greater transparency.

Tackling complexity with agentic AI

Today’s IT environments are sprawling across cloud, hybrid, and on-prem stacks. With that complexity comes a set of persistent challenges:

• Signal noise and alert fatigue
• Tribal knowledge trapped in silos
• Delays in diagnosing and resolving critical issues

By deploying AI agents that triage issues, surface relevant insights, and preserve operational knowledge, Devoteam is able to dramatically improve mean time to resolution (MTTR)—and reduce the human burden of making sense of chaos.

LogicMonitor + Edwin AI in action

To deliver this at scale, Devoteam relies on LogicMonitor as its unified observability platform—one tool covering AWS, Google Cloud, Microsoft Azure and on-prem environments. With Edwin AI integrated into that stack, the Devoteam team gets three key advantages:

• Fewer false positives through smarter alert correlation
• Clarity and context, not data dumps
• Continuous innovation, as LogicMonitor evolves Edwin AI

This three-pronged approach enables Devoteam to keep service levels high, even as client environments grow more complex.

Use case: From 3,000+ incidents to under 400

One standout customer story: a large enterprise transitioning off a legacy SI provider. Within six months of onboarding LogicMonitor and Edwin AI, Devoteam cut the customer’s monthly incident count from over 3,000 down to under 400.

This story is just one example of a measurable, sustainable transformation resulting from Edwin AI:

• 90%+ reduction in incident noise
• Fewer 3 a.m. escalations
• Significant on-call cost savings
• And most importantly: a major lift in customer satisfaction and NPS

This is what agentic AI looks like when it’s implemented with purpose.

Looking ahead: Devoteam’s vision for agentic AIOps

Devoteam sees AI agents evolving well beyond support roles to become real-time analysts, predictive troubleshooters, and infrastructure-aware advisors. In the coming years, AI won’t only assist; it will advise, anticipate, and architect.

According to Gaël Grootaert,“[AI agents] will be 10x or 100x faster than a human… able to manage complex problems.” In other words, they will soon be capable of simulating and evaluating multiple remediation paths before incidents even surface, proactively shaping how systems operate rather than simply reacting when things go wrong.

This evolution will move agentic AIOps into new territory:

• Proactive incident prevention, not just rapid resolution.
• Dynamic infrastructure recommendations based on telemetry, not static best practices.
• Automated scenario modeling to preempt service disruptions.

And for MSPs looking to stay competitive, the call to action is unambiguous. As Gaël put it: “You have no choice. It’s not a matter of when, it’s a matter of how.”

In Devoteam’s view, that proof comes through intelligent automation, outcome-driven AI, and a relentless focus on delivering value at scale. That’s not where the market is going—it’s already happening.

Advice to MSPs and IT leaders: Stop waiting, start proving

If there was one clear message from Devoteam during the webinar, it was this: get off the sidelines. AI agents are delivering real business impact today. For MSPs and IT leaders, the only question is how quickly you’re willing to move.

Next steps:

• Start now, even if it’s small. Run a proof of concept. Pick a noisy, manual process and test automation through AI agents. You don’t need a moonshot to prove value.
• Bring security and IT into the room early. AI adoption doesn’t succeed in silos. From risk assessment to tooling integration, internal alignment is critical—especially in MSP environments where trust is everything.
• Anchor on outcomes, not hype. If you can’t tie AI initiatives to specific, measurable improvements—lower incident volume, faster resolution, fewer escalations—it’s not ready. The good news? Those metrics are within reach.
• Lean on partners who’ve done it. Workshops, guided deployments, and AI-native platforms like LogicMonitor can shorten your learning curve and help you scale what works.

Experimenting with Agentic AI is about proving value. And the fastest path to proving value is to start solving real problems—now.

Agentic AI is here—Are you ready to act?

Agentic AI represents a foundational shift in how work gets done. Enterprises and MSPs that embrace this shift will scale faster, operate smarter, and gain resilience in ways traditional tools simply can’t offer.

The message from OpenAI, LogicMonitor, and Devoteam was clear: this is no longer about experimentation—it’s about execution. AI agents are already resolving tickets, preventing outages, reducing noise, and turning data into decisions in real time.

At the center of this momentum is Edwin A, a purpose-built platform for agentic AIOps that brings together unified observability, generative AI, and real automation to deliver tangible outcomes.

• Fewer alerts.
• Faster resolution.
• Better customer experience.

Now’s the time to see what Edwin can do for your IT operations. Get a demo.