What is Agentic AI? What To Know About This New AI Type

Artificial intelligence (AI) is no longer just a clever tool that can predict our next business move, power chatbots, or craft compelling email copy—it’s taking decisive actions autonomously. Welcome to the era of agentic AI, a groundbreaking advancement that’s turning AI solutions into proactive problem solvers capable of navigating complex tasks without constant human oversight.

Agentic AI is rapidly becoming the backbone of smarter, more dynamic business processes across industries. It goes far beyond scripted, deterministic automation of manual, repetitive tasks. Agentic AI produces intelligent agents capable of understanding complex problems, reasoning out the conditions, planning solutions, and adapting to changing environments in real time.

In this comprehensive guide, we’ll demystify the magic of agentic AI, explore how its AI capabilities differ from traditional types of AI, like generative AI (GenAI), uncover the mechanics behind its intelligent autonomy, and illustrate its potential for unprecedented transformation using real-world applications.

By the end, you’ll understand why all types of organizations, from healthcare to finance, are urgently adopting agentic AI to drive innovation and streamline more efficient processes.
 

• Agentic AI defined
• What is the difference between agentic AI and traditional AI?
• How does agentic AI work?
• Types of AI agents
• What is a multi-agent AI system?
• Common use cases for agentic AI systems
• How does agentic AI support intelligent IT automation?
• What are the benefits of agentic AI for IT?
• Why agentic AI is the next big thing

Agentic AI defined

Agentic AI—also known as autonomous AI—is a framework in which AI agents observe, plan, and act based on the changing real-time conditions in their environment, memory, and context.

Since these AI agents continuously learn and adapt, they can operate in non-predefined environments and solve problems by making dynamic decisions. This continuous learning and adaptability are critical for enhancing operational efficiency, improving decision making, and reducing manual oversight.

By integrating seamlessly with existing tools, ecosystems, and workflows through APIs, connectors, or other integration methods, agentic AI also enhances performance and efficiency across diverse applications. Its behavior is driven by its ability to reason and execute actions independently using advanced algorithms, machine learning, and real-time data processing to analyze information and interpret context using vast amounts of data from various sources. Real-time data is especially crucial for enabling AI agents to make timely and accurate decisions.

[Read also: Ultimate guide to AI cybersecurity—The benefits, risks, and rewards]

With this understanding of what agentic AI is, let’s delve into the key differences between agentic AI and traditional AI. This comparison will highlight how agentic AI’s capabilities and functionalities set it apart from the conventional AI systems you may already know.

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What is the difference between agentic AI and traditional AI?

Traditional AI is largely reactive, depending on defined rules and human input to execute tasks. Generative AI applications, another popular subset, focus on creating content in different formats—such as text, images, and code—from patterns within extensive datasets. However, agentic AI takes autonomy further, orchestrating actions and adapting dynamically to achieve predefined goals.

In 2025, we’ll begin to see a shift from chatbots and image generators towards “agentic” systems that can act autonomously to complete tasks, rather than simply answer questions.¹

Unlike traditional AI tools, which depend heavily on explicit human commands, agentic AI systems can make reasoned, informed decisions autonomously with minimal human intervention while providing transparency and actionable insights when needed through detailed logging and reporting mechanisms that allow users to understand the decisions made by the AI agents.

[Read also: 12 AI terms you (and your flirty chatbot) should know by now]

Having explored the fundamental differences between agentic AI and traditional AI, let’s focus on understanding how agentic AI operates. The following section will unravel the inner workings of agentic AI, shedding light on the mechanisms and processes that enable it to perform tasks with remarkable autonomy and intelligence.

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How does agentic AI work?

Agentic AI combines the predictive accuracy of large language models (LLMs), natural language processing (NLP), machine learning (ML), and reinforcement learning to derive decisions independently.

Agentic AI operates through a sophisticated, multi-step process that can be summarized into four key areas:

1. Perception: Agentic systems ingest and analyze massive amounts of data from sensors, databases, and other digital interfaces. They identify relevant patterns and features essential to making more informed decisions.
2. Reasoning: Utilizing LLMs for natural language understanding and generation, these systems interpret tasks, coordinate with specialized models for tasks, such as visual processing or data retrieval, and formulate action plans.
3. Action: Integrating through APIs with external software and tools, agentic AI executes tasks swiftly. Crucially, these systems operate within defined guardrails set by human operators, ensuring reliable, ethical, and safe decision making.

Agentic automation maintains a human in the loop—meaning the agents are intentionally limited in their ability to take certain actions that would require human intervention.²

4. Learning: Through continuous feedback loops, agentic AI systems improve over time, enhancing their effectiveness through ongoing data-driven refinements and adaptations.

This cyclical approach empowers agentic AI with robust decision-making capabilities, enabling it to handle complex tasks with increasing autonomy and precision.

[Read also: How enterprises are using AI to improve cybersecurity]

After diving into the mechanics of agentic AI, it’s time to meet the various AI agents that bring this technology to life. In the next section, we’ll deepen our understanding of how agentic AI tackles a wide array of tasks and challenges with such versatility and complexity by exploring the different types of AI agents and how each has its unique role and functionality.

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Types of AI agents

Agentic AI “agents” are specialized AI-powered programs that perform specific tasks autonomously. AI agents are classified based on their level of intelligence, decision making processes, and how they interact with their surroundings to reach desired outcomes.

There are several types of agents, each with unique capabilities and purposes, including:

• Reflex agents respond to specific stimuli with predefined actions, making them ideal for simple, reactive tasks. They operate based on direct responses to environmental conditions, following predefined rules without considering past experiences or future consequences.
• Goal-oriented agents achieve specific goals by planning and executing actions that lead to desired outcomes. They can evaluate different strategies and select the best course of action to reach their objectives.
• Learning agents continuously improve their performance through experience and adaptation, allowing them to handle more complex and dynamic environments by learning from past interactions and adjusting their behavior accordingly.
• Interactive agents interact with users or other systems dynamically and responsively. They can engage in conversations, provide recommendations, and adapt their behavior based on user feedback and customer queries. Interactive agents are commonly used in customer service, virtual assistants, and interactive learning environments.
• Collaborative agents work together with other agents or humans to achieve shared goals. They can communicate, negotiate, and coordinate their actions to optimize collective outcomes. Collaborative agents are essential in multi-agent systems where teamwork and cooperation are required.

AI agents represent a significant advancement in AI technology, offering a versatile and powerful tool for automating various tasks. By leveraging these different types of agents, organizations can enhance their operational efficiency, improve decision making, and reduce the need for manual oversight.

[Read also: How AI for automation will revolutionize today’s IT workflows]

While you can see how each agent type contributes to the mission of agentic AI, the story doesn’t end here. Imagine the power of these agents working together in harmony, amplifying their benefits through systems consisting of several intelligent agents working together to achieve individual and collective goals.

Next, we’ll dive into the concept of multi-agent systems, exploring how these interconnected agents enhance the capabilities and efficiency of agentic AI. By understanding the dynamics of multi-agent systems, we can appreciate the powerful synergy that drives innovative solutions and complex problem-solving.

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What is a multi-agent AI system?

Multiple AI agents, also known as multi-agents, can complete a variety of functions, such as aggregating data, monitoring for certain conditions, or executing predefined tasks. The primary goal of AI agents is to operate independently, make decisions, and take actions with minimal human intervention while coordinating and communicating effectively.

The combination of different agent types in agentic AI and the collaborative nature of multi-agent AI systems can significantly enhance an organization’s ability to operate efficiently, make informed decisions, and reduce the need for manual oversight. This approach leverages the strengths of individual agents while fostering collaboration and adaptability, ultimately leading to more robust and effective AI-driven solutions.

Each agent in a multi-agent system has a specific role and skill set, which enables them to address complex problems by making decisions and taking calculated actions. This collaborative approach mimics human teamwork, where specialized tasks are assigned to individual agents, and seamless communication is fostered to achieve the overarching goal.
 

[Listen to the podcast: Ethical AI—How (and how much) we can rely on it with guest and founder of SEO AGI Josh Bachynski, who designs and reverse-engineers AI]

By integrating multiple agents with different capabilities, these highly scalable and flexible AI systems have the potential to transform a number of diverse, real-world scenarios and applications, such as transportation, how organizations interact with customers, and even healthcare. However, this transformative potential is not just theoretical; it is already being realized in several key areas.

Let’s explore these everyday use cases in greater detail, highlighting how agentic AI systems are revolutionizing various industries and paving the way for a more intelligent and interconnected future.

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Common use cases for agentic AI systems

Agentic AI is reshaping industries, from logistics to healthcare, by managing tasks traditionally requiring significant human effort and oversight. Examples of agentic AI include:

• Autonomous vehicles: Agentic AI systems analyze real-time data to navigate complex traffic environments independently, adapting dynamically to new information to ensure safety and efficiency.
• Robotic process automation (RPA): Unlike traditional RPA, agentic automation tackles dynamic, unstructured processes to optimize and automate complex workflows with intelligent decision-making capabilities.
• Smart assistants: AI-powered agents handle nuanced interactions in customer service, providing personalized responses and performing tasks autonomously, vastly improving customer experiences.
• Financial robo-advisors: Financial institutions employ agentic AI to manage investment portfolios autonomously, using real-time analysis to offer customized investment strategies and ensure regulatory compliance.
• Patient monitoring: Agentic AI monitors patient data continuously, recommending treatments and adjusting care plans autonomously, significantly enhancing patient outcomes.
• Workflow management: At its core, workflow management is the coordination and optimization of processes and tasks across departments to achieve operational efficiency. Agentic AI dramatically elevates this practice by independently managing complex, dynamic workflows.
• Cybersecurity: Agentic AI is revolutionizing cybersecurity efforts by providing real-time insights and predictive analytics to identify and mitigate potential threats. These AI systems can detect anomalies and vulnerabilities by continuously monitoring and analyzing endpoint data, enabling proactive responses to security incidents. Additionally, agentic AI enhances incident response by automating the detection and resolution of security issues, reducing the time and effort required for manual intervention. This results in a more resilient and secure IT environment, safeguarding organizations against evolving cyber threats.

[Read also: Lean into autonomous incident response with Endpoint Reactions]

Agentic AI systems are already making significant impacts across various industries. From enhancing cybersecurity to optimizing customer service, the versatility and effectiveness of agentic AI are undeniable.

Now, let’s delve deeper into how agentic AI is revolutionizing IT operations through intelligent automation. By leveraging advanced AI capabilities, organizations can streamline complex processes, improve decision-making, and adapt swiftly to changing circumstances. The following section will examine how agentic AI supports intelligent IT automation, driving efficiency and innovation in the digital landscape.

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How does agentic AI support intelligent IT automation?

While automation is concerned with following predefined steps, agents have the autonomy to form their own paths and accomplish a task with or without user input.

For instance, think about the many manual steps in endpoint management and security. The tasks aren’t always straightforward—triaging hundreds of alerts that flow into the security operations center (SOC). Not all these tasks can be automated, which requires organizations to have large SOCs and a lot of humans to research alerts, get more context, make a decision, and act—this is a lot of noise, manual work, and cost with a high potential for error.

An AI agent can decompose the alert, analyze its artifacts, and collect additional data (as needed) to provide an assessment with the next steps, eliminating manual triage.

In IT operations, agentic AI is particularly impactful at streamlining complex tasks such as cybersecurity threat response, software development, and supply chain management. These intelligent systems independently identify anomalies, execute solutions, and learn from interactions to prevent future disruptions proactively.

Agentic AI automates software deployments, system updates, security monitoring, and incident responses by swiftly adapting to real-time changes without human prompts, which reduces errors, enhances agility, and frees IT teams to focus on higher-level strategic initiatives.

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What are the benefits of agentic AI for IT?

Agentic AI provides compelling benefits for IT by addressing complex, evolving challenges more effectively than traditional methods, including:

• Optimizing efficiency: By automating intricate, multi-step processes, organizations can significantly reduce the time spent on routine maintenance and manual interventions.
• Enhancing decision-making: Leveraging agentic AI systems can allow more accurate, informed decisions to be made autonomously, improving the overall speed and quality of IT responses.
• Improving adaptability: Quickly adjust to changing circumstances in real time, effectively managing unexpected disruptions and evolving requirements without manual oversight.
• Providing predictive insights: Identify potential failures, security breaches, and compliance risks before they materialize, enabling proactive measures and minimizing operational downtime.
• Ensuring cyber resilience: Rapidly detect and mitigate cybersecurity threats, with the ability to continuously reinforce defenses through adaptive learning.
• Accelerating threat detection: Quickly discover and respond to security anomalies, significantly reducing response times and limiting potential damage.
• Reducing false alarms: Accurately distinguish between genuine threats and benign events, reducing the workload and alert fatigue of IT and security teams.
• Streamlining evidence gathering: Automate data collection and analysis during incidents, accelerating investigations and response times.
• Minimizing mistakes: Automating routine and complex tasks reduces human error, ensuring greater consistency, accuracy, and reliability across IT operations.

The transformative impact of agentic AI on IT operations is undeniable, and the adoption of agentic AI is set to become essential to maintaining competitive advantage and operational excellence. Just as electricity revolutionized industries in the past, agentic AI is poised to become the driving force behind modern IT innovation. Its ability to enhance efficiency, improve decision making, and bolster cybersecurity measures makes it as indispensable to today’s organizations just as electricity was to the industrial era.

In the following section, we’ll discuss how your organization can effectively embrace this revolution by exploring how agentic AI can propel your organization into a future of intelligent automation and unparalleled success.

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Why agentic AI is the next big thing

Agentic AI is poised to revolutionize how organizations operate by bringing autonomous intelligent workflows to the forefront of business transformation. By leveraging real-time data and advanced AI capabilities, agentic AI systems can autonomously solve complex, multi-step problems, significantly enhancing efficiency and decision-making across various domains.

These systems enable faster incident resolution, proactive risk management, and seamless automation, revolutionizing IT, security, and risk operations. The ability to adapt to changing circumstances in real time and provide predictive insights makes agentic AI indispensable for modern businesses. Organizations embracing intelligent autonomous systems will gain a decisive edge, efficiently handling complex, adaptive processes with speed and precision unmatched by traditional methods.

As organizations embrace AI technology, they unlock unprecedented opportunities for innovation and growth, driving down costs and minimizing risk with unmatched confidence and precision. Agentic AI is not just the next big thing; it is the key to a smarter, more efficient, and autonomous future.

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Tanium has developed robust real-time data and actionability with AI and intelligent automation that can dynamically select the best approach to solve complex problems, such as deciding whether to push out a new patch update or change a registry key value.

These AI-driven automation capabilities, including our integration with ServiceNow agentic AI, utilize real-time data to interpret tasks and coordinate with specialized models for security incident analysis and risk assessment tasks. Continuous feedback loops are also a critical component, allowing these solutions to improve over time through ongoing data-driven refinements.

Additionally, our AI agent integration for Microsoft Security Copilot is transforming the cybersecurity landscape by autonomously managing high-volume security tasks and integrating seamlessly. This innovative approach provides actionable insights for security incident analysis and risk assessment by dynamically connecting to endpoints and intelligently parsing real-time and historical telemetry for automated analysis.

Our agentic triage process can also gather additional data, summarize the incident, and recommend remediation steps, significantly reducing the time required for manual intervention. Using Tanium, analysts can quickly eliminate false positives and focus on real threats, saving hours of work and preventing alert fatigue.

Tanium Autonomous Endpoint Management (AEM) exemplifies the practical implementation of AI for IT automation. By leveraging real-time data and autonomous workflows, Tanium can automatically detect, analyze, and resolve IT and security incidents, minimizing manual intervention. Our platform provides comprehensive endpoint visibility by delivering proactive insights to mitigate and remediate IT and security risks in real time with predictive capabilities that anticipate potential IT failures, security breaches, and compliance issues, which transitions management efforts from reactive and disruptive to proactive and self-healing.

In an era where real-time data is essential for effective IT and security management, Tanium provides the visibility needed to make informed decisions and prioritize actions swiftly. Tanium AEM provides this clarity where you need it most, with the ability to scale seamlessly across an organization and integrate with diverse tools and platforms to maintain consistent automation, governance, and unified IT and security management that adheres to industry standards and regulations, ensuring data privacy and robust security.

Schedule a demo today and discover how Tanium AEM can elevate your operations, enhance security, and drive innovation across your organization.