Agentic AI: How It Works and 7 Real-World Use Cases

What Is Agentic AI?

Agentic AI refers to artificial intelligence systems equipped with autonomy and decision-making capabilities. These systems can interpret data, learn from interactions, and drive decisions or actions without explicit human intervention. 

Agentic AI stands in contrast to traditional AI, which is narrowly focused on specified tasks, and existing generative AI systems which are limited to generating text or media, and are unable to carry out real-world actions. Agentic AI incorporates a broader understanding of contexts and objectives. It self-directs based on goals and input, providing a more adaptive interaction framework, and is able to take action in the real world, such as interacting with web-based systems, or operating robotics.

Agentic AI operates dynamically, adjusting its behavior based on new information and achieving objectives with a level of autonomy not possible in traditional AI systems. This results in improved capabilities for industries looking to automate complex processes.

This is part of a series of articles about AI cyber security.

The Evolution of Agentic AI 

Agentic AI has evolved from rule-based systems and reinforcement learning models to more sophisticated architectures that integrate large language models (LLMs) with external environments. Earlier AI systems relied heavily on predefined rules or extensive training with human-annotated data, which limited their adaptability. In contrast, modern agentic AI leverages the common-sense reasoning of LLMs to handle novel situations with greater flexibility.

The development of agentic AI follows a progression from simple text-processing models to more autonomous cognitive systems:

• Text processing models: Traditional LLMs, such as those used in natural language processing (NLP), generate text based on input prompts but lack real-world interaction capabilities.
• Language agents: These systems integrate LLMs with external environments, enabling them to process observations, make decisions, and take actions.
• Cognitive AI agents: The most advanced form, these agents not only interact with external environments but also manage internal reasoning processes, enhancing their ability to plan and adapt dynamically.

Agentic AI primarily operates within digital ecosystems, such as desktop and mobile operating systems, APIs, and web-based applications. These environments allow AI agents to perform tasks like searching the web, executing code, or interfacing with other software.

The next phase of agentic AI involves transitioning from digital to physical environments, where AI agents will interact with real-world objects and spaces. This shift will require advancements in sensory integration, robotics, and real-time decision-making, paving the way for AI systems capable of navigating dynamic physical settings and performing complex real-world tasks.

How Does Agentic AI Work?

Agentic AI operates through a structured process that allows it to interpret data, make informed decisions, execute tasks, and refine its capabilities over time. NVIDIA proposes the following process framework for agentic AI systems:

1. Perceive: The AI gathers and processes data from various sources, including sensors, databases, and digital interfaces. This involves extracting meaningful information, recognizing patterns, and identifying key entities in its environment. By analyzing inputs, the system builds a contextual understanding that informs its next steps.
2. Reason: A large language model (LLM) serves as the AI’s reasoning engine, orchestrating decision-making and coordinating specialized models for tasks. Techniques like retrieval-augmented generation (RAG) improve accuracy by allowing the system to access proprietary data sources. This step ensures that the AI generates relevant, context-aware solutions for complex problems.
3. Act: Once a plan is formulated, the AI executes tasks by integrating with external tools and software through application programming interfaces (APIs). Built-in guardrails help regulate actions, ensuring compliance with predefined rules. For example, an AI-powered customer service agent might process claims up to a set limit while flagging higher-value claims for human review.
4. Learn: Agentic AI continuously improves through a feedback loop known as the “data flywheel.” As the system interacts with users and processes new information, it refines its models to improve performance. This iterative learning process allows the AI to adapt, optimize decision-making, and increase operational efficiency.

Tips from the expert

Steve Moore is Vice President and Chief Security Strategist at Exabeam, helping drive solutions for threat detection and advising customers on security programs and breach response. He is the host of the “The New CISO Podcast,” a Forbes Tech Council member, and Co-founder of TEN18 at Exabeam.

In my experience, here are tips that can help you better leverage and implement agentic AI effectively:

1. Prioritize AI explainability for trust: Implement interpretability frameworks to make agentic AI’s reasoning more transparent. Use tools like SHAP (Shapley Additive Explanations) or LIME (Local Interpretable Model-agnostic Explanations) to provide insights into why decisions are made.
2. Combine agentic AI with reinforcement learning: While most agentic AI systems rely on supervised learning, reinforcement learning can improve adaptability in dynamic environments. Training AI through reward-based mechanisms enhances long-term strategic decision-making.
3. Deploy adversarial testing against agentic AI: Test AI resilience by exposing it to adversarial inputs and simulated attacks. This helps identify vulnerabilities in decision-making logic and ensures robust AI performance under unexpected conditions.
4. Integrate domain-specific ontologies for better reasoning: General AI models lack deep domain expertise. Embedding structured knowledge bases and ontologies (e.g., SNOMED for healthcare, FIBO for finance) enhances the AI’s contextual reasoning in specialized fields.
5. Enable human-in-the-loop (HITL) oversight: Even highly autonomous systems should have mechanisms for human intervention. Implement configurable thresholds where AI pauses and requests human validation for high-risk decisions.

Agentic AI vs. Generative AI

Agentic AI and generative AI serve different functions, though they share underlying machine learning technologies. Generative AI focuses on content creation, producing text, images, or other media based on input prompts. It operates reactively, generating outputs without making autonomous decisions or taking independent actions. When used in cybersecurity, it is a reactive assistant to the SOC analyst.

Agentic AI extends beyond content generation to decision-making and autonomous execution. It perceives its environment, reasons through complex scenarios, takes action, and learns from feedback. While generative AI models like OpenAI GPT-4o or Anthropic Claude create text based on learned patterns, agentic AI systems integrate decision-making frameworks, enabling them to operate independently within dynamic environments.

A key distinction is that generative AI outputs are typically static responses to user inputs, whereas agentic AI adapts its behavior over time, actively pursuing objectives. However, generative AI is a key component within modern agentic AI systems, providing language-based or image-based reasoning, suggesting actions, or generating content as part of a larger autonomous process. When used in cybersecurity, agentic AI goes beyond assisting analysts by autonomously investigating threats, correlating detections, prioritizing risks, and executing response actions—reducing manual workload and accelerating threat remediation.

Agentic AI vs. AI Agents

AI agents are software programs designed to perform tasks autonomously, often following predefined rules. Examples include chatbots, recommendation systems, and robotic process automation (RPA) bots, which execute tasks based on input conditions but lack advanced reasoning or adaptability. For example, an AI agent in cybersecurity might automatically flag suspicious login attempts based on static rules but require human intervention to analyze and respond to the threat.

Agentic AI is a newer concept, representing purpose-built systems that exhibit a higher degree of autonomy and problem-solving ability. Agentic AI does not simply follow fixed rules but dynamically adjusts its behavior based on contextual understanding. This enables it to navigate complex environments, make independent decisions, and learn from interactions. All agentic AI systems are AI agents, but not all AI agents qualify as agentic AI. For example, an agentic AI in a SOC could investigate an alert, correlate threat signals across multiple systems, determine the likelihood of an active attack, and proactively execute mitigation actions without requiring human intervention.

Real-World Applications of Agentic AI 

1. Automating IT Support and Service Management

Agentic AI is transforming IT support by proactively identifying and resolving issues before they escalate. Unlike traditional AI, which follows predefined troubleshooting scripts, agentic AI dynamically adapts to new problems by analyzing data from IT management systems, learning from past incidents, and autonomously adjusting its approach.

One key advantage is autonomous self-service, where employees can receive immediate IT assistance without waiting for human intervention. These AI-driven systems can handle routine tasks like password resets, software installations, and access provisioning while also integrating with various enterprise systems to diagnose and resolve more complex technical issues.

Additionally, agentic AI enhances problem resolution by continuously learning from interactions and integrating real-time data from multiple sources. This allows IT support systems to proactively address potential failures, reducing downtime and improving overall system reliability. By automating both common and advanced IT tasks, organizations can improve efficiency and free up IT teams to focus on more strategic initiatives.

2. Enhancing HR Operations and Employee Support

Agentic AI is improving HR operations by automating routine administrative processes while providing real-time, personalized support for employees. Unlike traditional rule-based AI, which delivers static responses, agentic AI can interpret context, evaluate employee inquiries, and generate tailored solutions based on specific needs.

One application is streamlining recruitment by automatically screening resumes, identifying top candidates, and scheduling interviews. This reduces manual workload for HR professionals and speeds up the hiring process. Agentic AI also enhances employee support by answering HR-related questions, assisting with benefits inquiries, and helping with onboarding processes.

Additionally, AI-powered HR assistants can integrate with internal communication tools, allowing employees to access HR support within their existing workflows. By automating administrative tasks and improving responsiveness, organizations can enhance the employee experience and allow HR teams to focus on strategic priorities like workforce planning and talent development.

3. Streamlining Financial Processes and Decision-Making

In the financial sector, agentic AI is optimizing decision-making and automating complex processes by analyzing large volumes of data in real time. These AI-driven systems can handle tasks such as expense reporting, compliance checks, fraud detection, and financial forecasting with high accuracy and efficiency.

One of the key benefits of agentic AI in finance is its ability to provide personalized financial management. AI systems can analyze a customer’s financial history, detect spending patterns, and recommend actions to improve financial health. This includes automating money transfers to prevent overdraft fees, optimizing savings based on interest rates, and detecting potential risks before they impact financial stability.

By reducing the reliance on manual processes and improving data-driven decision-making, agentic AI helps financial institutions enhance operational efficiency, reduce costs, and provide better financial insights for both businesses and individuals.

Agentic AI Use Cases in Cybersecurity

4. Real-Time Threat Detection and Response

Agentic AI improves cybersecurity by autonomously identifying and mitigating threats in real time. These systems continuously monitor network traffic, analyze user behavior, and detect anomalies that may indicate malicious activity. By leveraging machine learning models, agentic AI can differentiate between normal operations and potential threats.

Once a threat is detected, the AI system initiates an automated response, such as isolating compromised endpoints, blocking malicious IP addresses, or triggering alerts for security teams. Unlike traditional security tools that rely on predefined rules, agentic AI adapts dynamically, refining its threat detection models based on evolving attack patterns. 

5. Adaptive Threat Hunting

Agentic AI should improve proactive cybersecurity strategies by autonomously hunting for threats within an organization’s systems. Instead of relying solely on signature-based detection methods, these AI systems analyze vast amounts of security data, looking for hidden patterns and indicators of compromise. 

By continuously learning from new attack techniques, agentic AI refines its ability to uncover emerging threats that may bypass conventional defenses. The AI-driven threat hunting process involves correlating data from multiple sources, such as logs, endpoint activity, and network traffic, to identify potential attack vectors. 

6. Offensive Security Testing

Agentic AI can revolutionize offensive security by autonomously simulating cyberattacks to test an organization’s defenses. These AI-driven penetration testing systems mimic real-world attack scenarios, identifying vulnerabilities in networks, applications, and cloud environments. By continuously assessing security controls, organizations uncover weaknesses early.

Unlike traditional penetration testing, which requires human expertise and periodic assessments, agentic AI enables continuous security testing. It adapts its attack strategies based on evolving security landscapes. The AI system can generate detailed reports on security gaps, recommend remediation strategies, and validate the effectiveness of implemented fixes.

7. Case Management

Agentic AI can simplify cybersecurity case management by automating the classification, tracking, and resolution of security incidents. When a threat is detected, the AI system logs relevant details, categorizes the incident based on severity, and assigns it to the appropriate security team members. 

By analyzing past cases, agentic AI can recommend optimal response strategies. These AI-driven case management systems also improve collaboration by integrating with security information and event management (SIEM) platforms and other tools. They provide updates on incident progress, automate reporting, and ensure compliance requirements are met.

Challenges and Risks in Agentic AI Implementation

Agentic AI presents several challenges, particularly regarding accountability, data privacy, and the risks of excessive reliance on autonomous decision-making. These concerns require governance frameworks to ensure AI systems align with ethical standards and organizational objectives:

• Accountability in autonomous decision-making: A major challenge with agentic AI is determining accountability when systems operate independently. Unlike traditional AI, which follows predefined rules, agentic AI makes decisions dynamically, sometimes with unintended consequences. This raises concerns about liability—whether responsibility falls on the AI developer, the deploying organization, or another party. 
• Data privacy and security risks: Agentic AI depends on vast datasets, often including sensitive information. Without strong data governance, there is a risk of unauthorized access, data misuse, or opaque decision-making. Compliance with regulations such as GDPR and CCPA is essential to ensure transparency and protect user data. 
• Over-reliance on autonomous systems: Excessive dependence on agentic AI can lead to the erosion of human oversight in critical decision-making. While AI excels at processing data and optimizing workflows, it may lack the nuanced judgment required for complex, high-stakes decisions. This is particularly concerning in sectors like healthcare, finance, and law, which involve ethical considerations.
• Ethical governance and transparency: To ensure responsible AI deployment, organizations must establish ethical governance frameworks that define AI roles, decision-making boundaries, and expectations for transparency. This includes clear documentation of AI logic, audit trails for decisions, and mechanisms to contest AI-driven outcomes. 

Customizing and Integrating Agentic AI Solutions

Successful deployment of agentic AI requires careful customization and integration into existing business infrastructure. This involves aligning AI systems with organizational objectives, optimizing data pipelines.

Key Customization Considerations

To maximize effectiveness, organizations must adapt agentic AI to their needs:

• Knowledge foundation: AI systems should be trained on domain-specific expertise and business processes to improve decision-making. This ensures that AI outputs align with organizational strategies and industry requirements.
• Infrastructure optimization: Integrating AI with enterprise databases, APIs, and digital workflows improves efficiency. A well-structured data architecture ensures smooth AI operations and minimizes integration challenges.
• Human-AI collaboration: Establishing clear roles and oversight mechanisms is essential. While agentic AI operates autonomously, human supervision is necessary to validate decisions and maintain accountability.

Best Practices for Implementation

To ensure a successful rollout, organizations should take a structured approach:

• Gradual deployment: Start with pilot projects to test AI capabilities in controlled environments before scaling up.
• Continuous monitoring: Track AI decisions in real time to ensure transparency and prevent errors.
• Security measures: Deploy layered security frameworks to protect AI systems from cyberthreats and data breaches.
• Collaboration with experts: Work with AI and cybersecurity specialists to refine integration strategies and uphold best practices.
• Defining AI boundaries: Establish clear distinctions between human and AI responsibilities to maintain control and accountability.

Agentic AI is a rapidly advancing tool for improving efficiency and decision-making, but its success depends on strategic implementation and continuous oversight. By combining AI-driven automation with human expertise, organizations can benefit from agentic AI while ensuring responsible and ethical use.

Agentic AI for Security with Exabeam

Agentic AI is redefining security operations by shifting from passive detection to proactive defense, enabling AI-driven systems to investigate threats, correlate data, and execute response actions autonomously. As cyberthreats continue to evolve, security teams need solutions that go beyond static AI assistants and deliver real-time, adaptive intelligence.

Exabeam Nova is purpose-built to meet these demands, acting as a force multiplier within the SOC. By automating investigations, reducing alert fatigue, and accelerating response times, it empowers analysts to operate more efficiently and effectively. With seamless integration into the New-Scale Security Operations Platform, Exabeam Nova eliminates the need for separate tools, ensuring AI-driven insights are embedded directly into existing security workflows.

Unlike traditional AI assistants, Exabeam Nova dynamically adjusts its investigative approach based on the severity and context of each threat. It delivers precise, actionable insights tailored to both frontline analysts and security leaders, ensuring that every stakeholder has the intelligence needed to make informed decisions. 

Exabeam Nova develops rich case summary notes within Threat Center that compile key threat indicators, related detections, and recommended actions thereby reducing the time analysts spend piecing together fragmented data. Built on the Exabeam proprietary Threat Classification Framework and leveraging ten times more training data than its predecessor, it provides deeper investigative knowledge and more accurate threat prioritization.

Security and compliance remain at the core of Exabeam Nova. Unlike other AI solutions that rely on external cloud training, Exabeam Nova ensures that customer data remains private and secure. No investigation details are used for model training, and all data is processed within the trusted Exabeam environment, maintaining compliance with industry regulations and safeguarding sensitive information.

Beyond investigations, Exabeam Nova enhances security posture evaluations through Outcomes Navigator, where it helps teams assess use case coverage, pinpoint gaps, and provide targeted recommendations to strengthen defenses. By analyzing log sources, behavioral models, and rule utilization, it enables organizations to continuously refine and optimize their security strategy.

As AI-driven cyberthreats grow in sophistication, organizations can no longer afford to rely on outdated security models. Exabeam Nova delivers a proactive, AI-powered security strategy that enables teams to detect threats faster, respond with greater confidence, and continuously strengthen their security posture. The future of security operations is here—and it’s powered by Exabeam Nova. For more information visit Exabeam.com.