Agentic AI vs AI agents: What is their relationship?

Throughout human history, human innovation has changed the technological landscape in which we live. From the industrial revolution to the dot-com boom and now the development of artificial intelligence, critical inventions have transformed the way that we interact with the world around us. Often, these changes take time – industrialization, for instance, took over a century. The creation and widespread adoption of the internet took three or four decades. But when it comes to AI, that timeline is much, much faster. 

AI entered the public sphere with the release of conversational AI bots like ChatGPT 1 in November 2022. ChatGPT, created by the company OpenAI, already had 1 million users in just 5 days and 100 million within two months. 

Now, less than three years later, AI has evolved in leaps and bounds from simple automation to goal-driven intelligence. The future of AI is agentic, autonomous, and goal-oriented. Agentic AI and AI agents, while often confused for the same thing, are actually two important pieces of the artificial intelligence puzzle that make effective automation happen. 

So how will agentic AI and AI agents change our day-to-day lives and work? What’s the difference between AI agents and agentic AI, and what does this mean for the IT business?

What is agentic AI?

Agentic AI is a form of artificial intelligence that operates autonomously, reasons and makes decisions, and adapts to its environment. It can do this in real-time, lessening the need for human intervention and enhancing productivity and efficiency in the workplace and beyond. Some of the key capabilities of agentic AI include… 

• Independent problem solving: Agentic AI tools can choose and execute the best action based on in-the-moment data and context. 
• Self-learning: Agentic AI tools actually get better over time. They can adapt based on experience and data.
• Multi-step automation: Unlike previous iterations of AI bots (like generative AI), agentic AI tools are able to go beyond basic task execution to implement multi-step processes.

Examples of agentic AI 

Agentic AI tools are quickly becoming the industry standard in IT and beyond. One example is AI-powered cybersecurity innovations that detect and neutralize threats, such as Atera’s Action AI™ suite of tools. 

These agentic AI allows organizations to adopt a more proactive security stance and predict breaches before they happen, mitigating reputational damage and keeping customer trust intact. Other agentic AI innovations include self-improving IT systems that troubleshoot issues proactively and AI-driven workflow automation that optimizes processes dynamically. 

What are AI agents?

AI agents are AI-powered entities that perform tasks using the technology of agentic AI. They can be reactive agents (like Atera’s Autopilot, which only acts following end-user requests) or proactive agents (like an agent that runs health checks in the background 24/7 without prompting and takes action when it discovers an issue). These AI agents are built on the capabilities of agentic AI, which allows them to… 

• Perceive their environment, gathering data about user inputs, system conditions, and so much more
• Decide on the best course of action using agentic AI reasoning that is aligned with achieving the program’s specified goals
• Act autonomously to achieve defined goals, reducing the need for human interaction and making workflows more efficient and productive
• Learn and improve performance over time

Essentially, AI agents implement the autonomous capabilities of agentic AI to achieve revolutionary results.  Experts across the tech industry are predicting big things for AI agents: 

Source: SkimAI

Examples of AI agents

One of the most popular use cases of AI agents is the AI helpdesk agents, which resolve IT tickets autonomously. Atera’s AI Copilot is a great example, as it can interact with end users to resolve common support requests without the need for human intervention. When human action is needed, AI Copilot can elevate the request to the appropriate technician. These capabilities ultimately help Atera users improve ticket resolution times tenfold. 

Source: Atera

Another example is the cybersecurity agent, which uses AI to detect and mitigate security threats in real-time. Customer support agents are AI chatbots that learn from interactions to refine responses going forward. When users interact with Atera’s AI Copilot agent in this manner, the agent can generate a short summary of the problem by gathering information from the user, making it easier for technicians to find a solution without wasting time on endless back-and-forth.

How AI agents leverage agentic AI

AI agents are able to leverage the functions of agentic AI to perform important tasks with real-world impacts. There are countless agentic AI uses in IT management and beyond, and AI agents provide the framework for these use cases to optimize workflows, as outlined in the table below. 

Agentic AI Feature	How AI Agents Use It
Autonomy	AI agents execute tasks independently based on real-time needs. They don’t require constant prompting and can act in accordance with their stated goals independently.
Decision Making	AI agents analyze situations and choose the most effective response. They work toward the goals laid out for them and are able to make choices based on real-time data and context.
Self Learning	AI agents refine their performance through experience and feedback. They get better over time, as in the previous example of a chatbot that refines its responses based on feedback.
Task Complexity	AI agents handle multi-step tasks, adapting their strategy as needed. This is a departure from previous iterations of AI tools, which could perform simple tasks but not multi-step processes.
Human Interaction	AI agents collaborate with humans, adjusting based on user input. While some people are scared of AI taking over human jobs, many experts predict that AI agents will actually create more human jobs in the long run.
Goal Oriented	AI agents act on preset goals in order to employ the autonomous decision-making capabilities of agentic AI. The agents’ decisions are based on their interpretation of real-time data and context and alignment with decided goals.

Real-world applications of AI agents

Using AI agents in the workplace is not just theoretical – it’s happening now! Let’s review some of the real-world applications of AI agents. 

IT and Cybersecurity

Agentic AI is taking off in the IT and cybersecurity world with functions like AI-driven IT help desks and threat detection agents that proactively identify and mitigate security risks. 

Help desk AI agents can diagnose and fix common tech issues, reducing overwhelming workloads for IT teams – that’s exactly what Atera customer Leeds United Football Club saw when implementing Atera’s AI tools. They were able to reduce ticket volume by 35% due to AI agents solving over one-third of problems autonomously.

Business and Operations

In the business world, intelligent agents streamline workflows through AI process automation. AI-driven chatbots and recommendation engines can also serve as sales and marketing agents. A common thread in agentic AI implementation is boosted productivity and efficiency. With predictive analytics, AI agents can also help businesses make informed decisions for the future. 

Healthcare and Research

There are virtually endless applications for AI in healthcare and medical research. AI medical assistants can help with diagnosing conditions and even managing patient care. Data analysis agents leverage the power of AI to extract insights from cast datasets, cutting down manual workloads and helping find solutions faster. AI agents can even collect and analyze data about inventory and asset management for healthcare providers. 

The future of AI agents and agentic AI

These applications are just the beginning – the future of AI agents and agentic AI is bright. We expect that AI agents will continue to evolve, becoming even more autonomous and sophisticated. The way that AI agents interact and integrate with human teams will likely also shift as these platforms become more like coworkers than simple tools. 

We also expect to see a rise in multi-agent systems, or networks of AI agents that can collaborate to solve complex problems. Of course, all of these developments must go hand-in-hand with ethical AI usage and security considerations. Ensuring that AI tools operate safely and transparently, avoiding systemic bias and other concerns, is crucial for the effective and beneficial use of these tools. 

While most organizations recognize the importance of using AI responsibly, IBM’s reporting shows that we still have a ways to go in achieving that goal: 

Source: IBM

See how agentic AI and AI agents can change the way you manage IT

AI agents are the tools that we use to implement the autonomous, goal-driven, decision-making capabilities of agentic AI. Rather than thinking of them as agentic AI vs AI agents, we should be considering how to use them in harmony.

That’s why in order to keep up with innovations and trends in AI, businesses must embrace AI agents – like Atera’s AI Copilot – to improve efficiency, security, and innovation. Stay ahead of the curve by adopting Atera’s agentic AI-powered solutions in your IT business with a 30-day free trial.