Agentic AI and AI agents are shaping conversations, product roadmaps, and businesses everywhere. In this blog, we’ll break down what each really means, explore the nuances and differences between agentic AI vs. AI agents, understand the value each brings, and see how MSPs can put them to work to deliver greater value to customers.
Even if you have been hiding under a rock with no internet signal, chances are the words AI agents and agentic AI have reached you. They are not just buzzwords, but transforming how IT leaders work, what we do, and how our team members engage with future technology.
For managed service providers (MSPs) and in-house IT service management (ITSM) teams, AI is not just helping IT teams anymore. It’s learning to run the show. With the rise of agentic AI ITSM practices, IT leaders are starting to rely on AI-driven autonomy to improve service quality and outcomes.
Over the last couple of years, automation and GenAI have already streamlined ticket resolution, patch deployments, and repetitive admin work, but agentic AI is taking things a step further. Modern teams are experimenting with agentic AIOPS frameworks to predict issues and self-heal environments, and are deploying MSP AI agents to handle high-volume routine tasks at scale.
But what exactly is agentic AI, and how does it differ from AI agents? Let us break it down in this Agentic AI vs AI agents blog.
What is agentic AI?
Agentic AI refers to artificial intelligence systems designed to operate autonomously, utilizing reinforcement learning to make informed decisions without constant human oversight. Unlike traditional AI agents that require explicit prompts or instructions, agentic AI can determine what tasks to execute, when to execute them, and how to adapt based on outcomes.
This type of AI focuses on strategic transformation and proactive workflows, enabling organizations- especially MSPs and IT teams- to move from reactive operations to forward-looking, outcome-driven management. Typical applications include proactive IT management, smart service desk operations, issue detection in IT infrastructure, and risk mitigation. As agentic AI ITSM capabilities mature, more organizations are shifting toward self-governing processes powered by autonomous reasoning engines.
What are the types of agentic AI?
Agentic AI can be categorized either by architecture- how they are structured- or by capabilities, which define how they interact with their environment and achieve objectives.
By architecture
Single-agent systems: One AI agent handles a task independently, ideal for well-defined, straightforward problems.
Multi-agent systems: Multiple agents collaborate to solve complex problems.
Horizontal systems: Agents work as equals, including virtual assistants, each specializing in a specific skill, including data analytics to complete a task collectively.
Vertical systems: A primary agent oversees and delegates tasks to subordinate agents, combining their outputs for final results.
Hybrid architectures: These systems blend different approaches to meet specific operational needs.
By capability
Simple reflex agents: React immediately to environmental inputs without memory of past states.
Model-based reflex agents: Maintain an internal representation of the environment, handling partial observations more effectively.
Goal-based agents: Make decisions to achieve specific objectives, often planning actions in advance.
Utility-based agents: Evaluate multiple possible actions and choose the one that maximizes overall benefit or “utility.”
Learning agents: Continuously improve performance by learning from past experiences and adapting to new information.
Other classifications
Reactive agents: Respond directly to stimuli from their environment.
Proactive agents: Take initiative, plan to reach goals.
User-assistant agents: Help users with tasks like scheduling, reminders, or answering queries.
Automated process management tools: Streamline workflows by automating repetitive tasks such as data entry or system monitoring.
What Agentic AI means for MSPs
Agentic AI can empower IT teams and MSPs to step into a more strategic role in any organization, allowing them to focus on driving improvements in IT infrastructure and enhancing business operations at scale. Instead of merely reacting to problems, ITSM teams and MSPs could use AI agents to oversee fixes while humans can focus on innovation and driving business impact.
With agentic AI, IT teams and MSPs can turn IT management from a reactive task to a proactive workflow and manage this efficiently at scale.
For example, when it comes to a process like incident response, agentic AI can go beyond fixing the issues based on pre-determined automation to analyzing real-time metrics, comparing historical patterns, and deciding the most efficient and effective way to handle the incident.
Similarly, with security and monitoring, agentic AI goes beyond just alerting to proactively assess risk levels, evaluate the situation against compliance benchmarks, and set remediation or updates in motion.
Essentially, with agentic AI, MSPs can operate with greater efficiency and scalability, turning reactive IT operations into autonomous, outcome-focused processes.
Agentic AI vs. AI Agents: What’s the difference?
Agentic AI is the concept of using AI to run processes independently and autonomously, achieving a larger objective without human intervention at every step. It can plan, reason, and adapt, and function in dynamic environments to make decisions in real-time.
AI agents are the tools that execute specific tasks and help achieve this goal. They respond to clear prompts to carry out tasks with speed and accuracy, but cannot decide what to do without human or agentic AI direction.
Simply put, agentic AI orchestrates the overarching goal, while AI agents do what it takes to get there and bring the goal to life. By taking the agentic AI approach, you can manage workflows or processes autonomously using multiple AI agents to run tasks that contribute to the larger objective.
Take service desk efficiency as an example. For MSPs and ITSM teams, the agentic AI goal might be to reduce the number of tickets reaching their technicians. To make that happen, multiple AI agents could be deployed, such as one to deflect L1 tickets, another to answer common queries, a third to route tickets to the right queues, and so on. Each agent tackles a slice of the work, but together they deliver on the broader vision.
The same applies to patch management. AI agents might be used to scan for updates, apply patches, and confirm installation. But within an agentic AI framework, patching will be a part of a broader organizational strategy designed to modernize IT security, ensure uninterrupted compliance, and provide complete protection for all assets.
AI Agents vs. Agentic AI: Which one does your MSP need?
AI Agents
As we saw earlier, AI agents are built to execute tasks that help in achieving a larger goal. They require prompts and clearly defined instructions, and while they can handle tasks faster and more efficiently than humans, they cannot make decisions without human input.
So, AI agents are best suited for situations where accuracy, consistency, and speed are important.
For example, AI agents are ideal for detecting issues in assets or other IT infrastructure, logging actions, updating tickets, and notifying stakeholders.
They are also ideal for improving efficiency in ticket handling by taking care of tasks such as classifying, prioritizing, and assigning tickets.
In several companies, AI agents are now being used to look through relevant knowledge base articles and handle basic L1 tickets without handing them to human technicians.
MSPs and IT teams can also deploy AI agents to keep an eye on compliance by running periodic checks, tracking hardware and software inventory, and flagging issues as and when they spot anomalies.
Agentic AI
This approach is better when organizations are working on larger strategic changes. It is not just about running tasks, but using AI to take informed decisions on what tasks to run, how to run them, and when to adapt to any changes to the status quo.
The focus with agentic AI is not on reducing repetitive manual tasks and improving speed, but on driving better outcomes.
For example, it is ideal to take the agentic AI route if you are looking to transform your IT management from a reactive to a proactive workflow, and do this at scale. Another application is with IT compliance to handle growing instances of cybersecurity breaches.
Agentic AI can help continuously monitor for vulnerabilities or misconfigurations, assess risks, and take corrective action before incidents occur. It is also useful for patch management as it can prioritize patches by risk level, coordinate safe deployment windows, and maintain an always-accurate inventory across client environments.
Feature / Aspect | Agentic AI | AI Agents |
Primary purpose | Drive autonomy in organizations and reduce human intervention | Execute tasks to achieve a larger goal |
Decision-making capability | Can make informed decisions on what tasks to run, how, and when to adapt | Cannot make decisions without explicit input or prompts |
Focus | Strategic transformation, outcome-focused actions, proactive workflows | Accuracy, consistency, speed, and efficiency in tasks |
When to use | Situations requiring strategic oversight, proactive IT management, and risk mitigation | Situations where repetitive, routine tasks need fast and accurate execution |
Typical use cases for MSPs and IT teams |
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Also read: Agentic AI vs. generative AI
What are the use cases for Agentic AI and AI Agents?
Agentic AI and AI Agents serve distinct purposes in organizations, particularly for MSPs and IT teams. Understanding their use cases helps deploy the right AI solution for maximum impact.
Agentic AI use cases
Proactive IT management: Anticipates issues in infrastructure before they impact operations.
Strategic decision-making: Analyzes trends to optimize workflows and resource allocation.
Smart service desk operations: Identifies high-priority incidents and automates escalation in real time.
Risk mitigation: Detects potential compliance or security threats and recommends actions.
IT transformation: Moves IT operations from reactive to proactive, outcome-focused workflows.
AI Agents use cases
Task automation: Executes repetitive or routine tasks with speed and accuracy using a pre-determined course of action.
Ticket handling: Classifies, prioritizes, assigns, and resolves basic L1 tickets.
Compliance monitoring: Performs scheduled checks and ensures adherence to policies.
Asset monitoring: Detects anomalies or issues in devices, servers, and networks.
Workflow optimization: Automates routine actions like logging, notifications, and updates.
What to consider before choosing Agentic AI or AI Agent?
Selecting between agentic AI and AI agents depends on task complexity, autonomy needs, ethical accountability, customer service quality aspects, and deployment considerations.
Factor | Agentic AI | AI Agent |
Complexity | Ideal for multi-step workflows requiring coordination across multiple components. | Best for well-defined, specific tasks handled by an internal or external tool. |
Autonomy & decision-making | Supports high autonomy, real-time adaptation, and probabilistic decision-making in uncertain environments. | Follows predefined rules and static logic; limited decision-making. |
Accountability | Complex systems may require careful ethical consideration, as tracing decisions can lead to unintended consequences making accountability challenging. | Easier to track accountability due to direct, simpler actions. |
Integration & deployment | Suitable for projects involving multiple agents; setup is more complex. | Simple to deploy standalone or as part of a larger system. |
How to choose
Single, specific tasks: Use an AI agent for straightforward execution, like a chatbot or data processing task.
Complex, multi-step projects: Choose agentic AI when coordinating multiple agents when you need to handle complex tasks and achieve a goal, such as researching, analyzing, and reporting.
High autonomy required: Agentic AI excels in environments requiring independent adaptation to achieve specific goals without constant human input demonstrating the capabilities of autonomous agents.
Simplicity and control: Opt for an AI agent to maintain tighter control over logic and accountability.
Ethical considerations: Evaluate potential risks of agentic AI’s autonomy, especially regarding decision accountability in coordinated systems.
Future-proof your IT with Agentic AI
After automation and GenAI, agentic AI marks the next stage in technological evolution for IT teams. It is time to move from merely executing tasks to driving impactful outcomes. And this starts with modernizing your IT infrastructure and processes with agentic AI.
SuperOps was built for exactly this. Our unified PSA-RMM platform, powered by agentic AI, scales with your business and works alongside you, thinking, adapting, and solving, not just executing.
See for yourself how SuperOps and agentic AI can transform your IT operations; schedule a demo with us today!
Frequently asked questions
Are AI agents smarter than agentic AI?
Not necessarily. AI agents excel at executing specific, well-defined tasks efficiently, while agentic AI is designed for complex, multi-step workflows requiring autonomy and decision-making. “Smarter” depends on context: agentic AI handles strategy and coordination, whereas AI agents focus on accuracy, speed, and consistency for routine operations.
Which industries benefit most from AI agents and agentic AI?
Industries with complex workflows and high operational demands benefit the most. MSPs, IT services, finance, healthcare, and logistics gain efficiency, accuracy, and proactive management. Autonomous AI agents streamline repetitive tasks, while agentic AI enables autonomous decision-making and workflow optimization, reducing human intervention and enhancing strategic outcomes across multiple sectors.
Is it safe to use AI agents with customers?
Yes, when properly configured. AI agents handling repetitive or straightforward customer tasks- like ticket routing, FAQs, or basic support- can improve efficiency and response times. However, sensitive or high-stakes decisions should involve human oversight. Implement monitoring, error handling, and security protocols to ensure reliability and maintain customer trust.
Can agentic AI make decisions instead of humans?
Yes, agentic AI can make autonomous decisions in structured environments, especially for multi-step workflows. It evaluates options, adapts to changing conditions, and executes tasks with minimal human intervention. However, organizations should monitor critical decisions, set boundaries, and maintain oversight for ethical, legal, or high-risk scenarios.
Is agentic AI the same as generative AI?
No. Generative AI creates content- text, images, code, or designs- based on prompts. Agentic AI, in contrast, autonomously makes decisions and coordinates actions across multiple tasks or agents. While generative AI can be a component within agentic systems, agentic AI emphasizes autonomy, workflow execution, and strategic decision-making, rather than just content generation.
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