33 agentic AI terms you should know if you work in IT

Developments in artificial intelligence are occurring at a rapid-fire pace, but it’s important to keep up with what’s new in AI if you want to ensure your company stays ahead of the curve. 

The latest innovation? Agentic AI — an AI model that can reason and operate autonomously, making decisions without the need for human input. Agentic AI can learn, adapt, make independent decisions, and take actions that are aligned with its preset goals, making it a substantial improvement from previous models. 

Agentic AI is already skyrocketing in popularity – one survey of senior technology leaders found that 93% of respondents are “very interested” in integrating agentic AI into the workplace. To keep your enterprise on the cutting edge of new technology and keep up with efficiency and productivity requirements, it’s crucial to understand common agentic AI terms. 

Today, we’re breaking down these key agentic AI terms by category to make them easier to understand. 

Core concepts of agentic AI

• Agentic AI: A type of AI that is capable of operating autonomously, learning, adapting, and making independent decisions and taking actions based on context and data in real time – within a defined scope, of course.
• Autonomous Agents: Specific agentic AI models and tools designed to gather data, make decisions, and execute tasks to achieve predefined goals. Autonomous agents put agentic AI in action. 
• Multi-Agent Systems: Often abbreviated as MAS, multi-agent systems are multiple artificial intelligence agents that are working together to perform tasks on behalf of another system or a user. 
• Goal-Oriented AI: Like agentic AI, goal-oriented AI tools work toward a certain goal and engage in multi-step processes. 
• AI Orchestration: The coordination and management of AI models, systems, and integrations. It refers to the overall deployment, implementation, and maintenance of multi-agent AI systems. 
• Decision-Making AI: Agentic AI has decision-making powers that can occur without human intervention, as it acts in pursuit of predefined goals. 
• Adaptive Learning: Agentic AI tools utilize adaptive learning, meaning that they are constantly analyzing real-time data and context to make informed decisions and improve outcomes over time. 
• Reinforcement Learning: A machine learning technique that trains AI tools to make the best possible decisions to optimize outcomes in an environment. 

Agentic AI tools, commonly seen as autonomous agents, utilize capabilities like decision-making AI and goal-oriented AI to make independent decisions with less human intervention. 

These agents can collaborate in multi-agent systems set up through AI orchestration, and they continuously learn through adaptive learning processes like reinforcement learning.  As these capabilities evolve, new agentic AI terms are emerging to describe how intelligent systems operate more autonomously in complex environments.

Agentic AI in IT automation and management

• IT Automation Agents: Autonomous agents can streamline IT management by automating routine tasks – like Atera’s Copilot, which automates many aspects of IT management. For example, Copilot allows for more efficient ticket resolution, which results in freed up time for technicians and happier end users. 

Source: Atera

• AI-Driven Troubleshooting: Part of the power of agentic AI is its ability to proactively identify issues and troubleshoot potential problems before they escalate. 
• Automated Patch Management: Patch management is an important component of maintaining system health, and agentic AI tools make it easy by implementing patches without the need for human intervention, which in turn keeps your network safer. 
• Intelligent Ticketing Systems: These systems utilize agentic AI to resolve many issues without human intervention. Intelligent ticketing systems can empower end users to resolve many basic issues without elevating the ticket to a technician – and when human intervention is needed, they can allocate the ticket to the right person to ensure more effective support and faster resolution times. 
• Autonomous Incident Resolution: By analyzing data and context, agentic AI tools understand when something has gone wrong and can resolve a problem without further prompting from a human actor. A support desk that offers autonomous incident resolution is often called an AI helpdesk.
• Predictive Maintenance: Over time, autonomous AI agents can perform predictive maintenance by understanding potential weak spots in a security system or keeping track of which assets may need repairs or replacement soon. 
• AI-Optimized Workflows: AI optimization helps make workflows more streamlined and efficient, thus allowing IT teams to focus on higher-level strategy. 

IT automation agents have countless real-world applications, like the ones described above, that streamline IT system management and make IT teams more productive. In turn, end user satisfaction rates also rise. 

Closely related to IT automation, let’s go over some common agentic AI terms in the cybersecurity world.

Agentic AI in cybersecurity

• AI Threat Detection Agents: AI agents that can detect threats and anomalies in cybersecurity and alert the proper channels to take action. 
• Autonomous Security Agents: These agents build on the alerts of AI threat detection agents to quarantine the issue and mitigate security threats before damage is done. 
• Behavioral Analysis AI: Cybersecurity uses behavioral analysis AI to learn, predict, and recognize behavioral patterns of malevolent actors. 
• AI-Powered Threat Mitigation: AI-powered threat mitigation allows IT teams to understand security threats and quarantine and resolve the issue before any damage occurs to the company’s data, customer trust, or reputation. 
• Attack Surface Management: A proactive cybersecurity strategy focused on continually identifying, monitoring, and reducing potential entry points for attackers through assessing risks across an organization’s IT assets and network. 
• Automated Incident Response: Automated incident response tools follow a preset sequence of actions when an incident alert is triggered to quarantine the problem and mitigate the threat, if possible. 
• Adaptive Cyber Defense: A proactive, real-time approach to cybersecurity that continually monitors an organization’s network and adapts to evolving threats, getting better over time. 

Agentic AI enhances cybersecurity by deploying autonomous security agents that collaboratively perform the tasks listed above. These agents can detect anomalies (AI threat detection agents), analyze user behavior (behavioral analysis AI), and automatically respond to potential threats in real time. 

Agentic AI in customer experience & communication

• AI Chatbot Agents: Using a mix of agentic AI and generative AI allows people to create AI chatbot agents that communicate with humans in a more natural way through natural language processing (NLP).
• Conversational AI Agents: These AI agents are trained on data sets with human dialogue, helping them understand human language patterns. As a form of conversational AI, they can also draw on company-specific data to answer questions from customers or end users. These tools don’t use agentic AI, which is important to note.
• AI-Powered Virtual Assistants: These AI agents use natural language processing models to respond through text and help complete tasks like booking, scheduling, emails, and other typically one-step processes. Agentic AI can help make AI-powered virtual assistants able to complete multi-step tasks. 
• AI-Generated Personalization: With agentic AI, advanced personalization abilities come into play through analyzing customer activity data. This trend is expected to have a huge impact in many sectors, particularly e-commerce, marketing, and sales. 
• AI-Driven Support Automation: Agentic AI agents can utilize both conversational and generative models to help end users and customers solve support-related problems without human intervention, freeing up human labor for more complex tasks. 

Agentic AI improves customer experiences by enabling chatbots and virtual assistants to engage in natural conversations, predict customer needs, and automate support tasks. With AI-generated personalization tools and AI-driven support automation, users will have better communication experiences that lead to rising customer satisfaction rates. 

Agentic AI in healthcare and business

• AI-Assisted Diagnostics: Agentic AI applications in healthcare can help diagnose patients by analyzing vast data sets. 
• Predictive Healthcare Agents: These AI agents can anticipate patient needs, predicting what a patient might respond to and helping to tailor treatment plans. There are many agentic AI applications in healthcare, as seen below: 

Source: Harvard Business Review

• Autonomous Patient Monitoring: AI tools in healthcare settings can analyze vast data sets to autonomously monitor patients. Detect anomalies that might present risks before a life-threatening situation occurs. 
• Autonomous Financial Advisors: Researchers at MIT Sloan School of Management are currently developing large language models that can give financial advice to individuals by analyzing data about their personal assets as well as market trend data. 

Source: MIT Sloan

• Autonomous Robotics Agents: These software systems enable robots to perceive their environments and make decisions based on data intake. There are countless applications for AI robotics, including self-driving cars, industry and manufacturing, healthcare, and more. 
• Fraud Detection AI Agents: Like behavioral analysis AI agents, these tools can help predict and detect fraud by analyzing patterns of behavior that suggest malevolent actors. These have many business and finance sector applications, in particular. 

Agentic AI tools have the capacity to completely change work and life as we know it across many different industries. In the AI healthcare space in particular, researchers have found that AI applications are almost boundless. AI spending in healthcare increased by a staggering 233% over the past three years.  

Stay ahead of the curve in your IT operations with agentic AI

As the role of agentic AI continues to grow and expand, with applications in diverse industries such as finance, IT, business, healthcare, and more, it’s more crucial than ever to understand agentic AI terms and use cases. 

Agentic AI will continue to evolve and shape future technology, and utilizing these efficiency-boosting models in your organization will help you remain on the cutting edge of development and productivity. 

For IT pros that want to save 11-13 hours in IT troubleshooting time per week, Atera AI is the solution you’ve been waiting for. Our Action AI™ suite of agentic AI tools automate ticketing, patch management, and a whole lot more – plus, enjoy advanced functions like remote session summaries, command-line generation, real-time device troubleshooting, instant ticket summaries, script generation, knowledge base connection, and more. Ready to make the most of agentic AI? Take Atera (and our full suite of AI tools) for a test drive today with a 30-day free trial, no credit card required.