Ops Explained: AIOps vs. DevOps vs. MLOps vs. Agentic AIOps
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 […]
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:
What DevOps, MLOps, AIOps, and agentic AIOps actually mean
How they fit into modern IT (and where they don’t overlap)
Why agentic AIOps marks a transformational leap for IT operations
Let’s start by understanding what each “Ops” term means on its own.
Why “Ops” Matters in IT Today
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.
DevOps bridges development and operations to accelerate application delivery.
MLOps operationalizes the machine learning lifecycle at scale.
AIOps brings intelligence into IT incident management and monitoring.
Agentic AIOps pushes operations further—moving from insights to autonomous action.
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.
DevOps, MLOps, AIOps, and Agentic AIOps: Distinct Terms, Different Challenges
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.
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.
Used by: Software developers, DevOps engineers
Purpose: Automate and accelerate the software delivery pipeline
DevOps automates the build-and-release cycle. It reduces errors, accelerates deployments, and helps teams ship with greater confidence and consistency.
AIOps consumes the telemetry—metrics, events, logs, and traces—that DevOps pipelines generate to power incident detection and analysis.
What is MLOps?
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.
Used by: ML engineers, data scientists
Purpose: Automate and govern the ML model lifecycle
Key Tools: MLflow, Kubeflow, TFX, SageMaker
Why MLOps Matters:
MLOps ensures machine learning models stay accurate, stable, and useful over time.
How MLOps Interacts with Other Ops:
Adapts DevOps principles, borrowing ideas like pipeline automation and versioning for model management.
Supports AIOps use cases by providing trained models that can detect patterns, anomalies, and trends across IT environments. MLOps and AIOps can work together, but they solve very different problems for different practitioners.
MLOps is not an extension of DevOps, nor is it a prerequisite for AIOps. It addresses a unique set of needs and typically operates in its own pipeline and toolchain.
What is AIOps?
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.
Used by: IT operations, SREs, NOC teams
Purpose: Improve system reliability and reduce mean time to resolution (MTTR)
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.
How AIOps Interacts with Other Ops:
Ingests telemetry from across the IT environment, including metrics, events, logs, and traces from systems managed by DevOps, but operates independently of DevOps workflows.
May use machine learning models—whether built-in, third-party, or homegrown—to improve anomaly detection and predictions, but does not rely on an internal MLOps process or teams.
What is Agentic AIOps?
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.
Used by: IT operations, SREs, NOC teams
Purpose: Close the loop between detection and resolution; enable self-managing systems
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.
How Agentic AIOps Interacts with Other Ops:
Extends AIOps capabilities, taking incident insights and acting on them autonomously.
Operates on telemetry from across the IT environment, including systems built and managed with DevOps practices.
May incorporate ML models to inform decision-making, whether those models are homegrown, third-party, or built into the platform.
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.
These “Ops” Areas Solve Different Problems—Here’s How They Overlap
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.
DevOps accelerates application delivery.
MLOps manages the machine learning model lifecycle.
AIOps brings intelligence into IT monitoring and incident management.
Agentic AIOps pushes IT operations toward autonomous resolution.
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 and MLOps: Shared ideas, different domains
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: Using telemetry from DevOps-managed environments (and beyond)
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.
Agentic AIOps: Acting on insights
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.
Bottom Line: Understanding the “Ops” Landscape
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.
Want to see what agentic AIOps looks like in the real world?
Get a demo of Edwin AI and watch it detect, decide, and resolve—all on its own.
Margo Poda leads content strategy for Edwin AI at LogicMonitor. With a background in both enterprise tech and AI startups, she focuses on making complex topics clear, relevant, and worth reading—especially in a space where too much content sounds the same. She’s not here to hype AI; she’s here to help people understand what it can actually do.
Disclaimer: The views expressed on this blog are those of the author and do not necessarily reflect the views of LogicMonitor or its affiliates.
