What is agentic AI?
Agentic AI refers to artificial intelligence systems designed to act with autonomy, pursue goals, and make decisions proactively—often across extended timeframes and in complex, changing environments.
Unlike traditional AI systems that respond to specific inputs or follow tightly scoped instructions, agentic AI operates more like an intelligent assistant that can initiate tasks, adapt plans, and even manage sub-tasks on its own to achieve a broader objective.
How is agentic AI different from traditional AI?
While traditional AI might identify objects in an image, summarize text, or answer a prompt, it usually does so within strict boundaries and only in response to a user request. Agentic AI, on the other hand, can:
- Set and pursue goals: It's designed to keep working toward an outcome, even if the path isn't clear from the start.
- Plan and adapt: It can break complex goals into steps, monitor progress, and shift strategies when things change.
- Act autonomously: It can initiate actions without needing a prompt for each one, often coordinating across systems or environments.
This kind of behavior makes agentic AI more "agent-like" – less like a tool, more like a collaborator.
How does agentic AI work in cybersecurity?
Agentic AI in cybersecurity isn't just smart – it’s typically self-directed. Rather than waiting to be told what to do, these systems can assess a situation, set a goal, make a plan, and take action, all in service of defending digital environments. Here’s how that actually works in practice:
Autonomous goal-setting and planning
At the heart of agentic AI is the ability to operate independently. In a cybersecurity context, this might look like:
- Detecting anomalies that suggest a potential threat
- Framing a goal like "investigate suspicious behavior on endpoint X"
- Creating a step-by-step plan to manage logs, correlate events, and identify root causes
Unlike traditional rule-based systems that follow a playbook, agentic AI can generate its own playbook based on the context it sees and adjust as conditions evolve.
Perception, decision-making, and action
Once the goal and plan are in place, agentic AI moves through a loop of:
- Perception: It ingests and analyzes live data – like network traffic, authentication attempts, or system logs – to understand what's happening.
- Decision-making: It evaluates options based on goals and constraints. Should it flag an analyst? Quarantine a device? Run a deeper scan?
- Action: It executes chosen steps autonomously, then loops back to update its perception and refines its plan if needed.
Different from narrow and generative AI
Most AI in use today is either narrow (good at one specific task) or generative (able to produce content like text or code). Agentic AI is something else:
- Narrow AI might detect malware but won't know what to do next.
- Generative AI might explain what malware is, or even write a detection rule, but it won't decide when or why to use it.
- Agentic AI, in contrast, can detect malware, analyze its behavior, decide whether to isolate a system, take action, and then explain what it did.
Real-world examples of agentic AI
Autonomous incident response agents
Some security platforms now include AI agents that can independently investigate and perform threat detection and response functions. For instance, after spotting suspicious lateral movement in a network, an agentic AI might:
- Launch its own investigation by pulling logs and checking for related activity
- Correlate signals across systems to confirm the scope of compromise
- Automatically isolate affected endpoints, block malicious IPs, and notify analysts - all without being prompted at each step
This kind of autonomous response reduces dwell time and speeds up containment, especially when human analysts are overwhelmed.
AI-driven phishing mitigation
Agentic AI can also defend against phishing in near real-time. When an employee clicks a suspicious link, an agentic system might:
- Inspect the URL, scan the destination site, and cross-reference with threat intel feeds
- Check whether credentials were submitted or if any downloads occurred
- Retroactively quarantine the email, alert the user, reset credentials, and adjust detection rules for similar future attacks
Cloud misconfiguration remediation
In cloud environments, misconfigurations can create serious security gaps. Agentic AI can patrol infrastructure for risky setups – like open storage buckets or overly permissive identity and access management (IAM) policies – and take steps to correct them.
These kinds of AI agents act like tireless digital custodians, scanning for issues, applying fixes, and helping prevent security drift.
Benefits of agentic AI
Agentic AI offers a fundamentally different approach to cybersecurity operations, one that emphasizes autonomy, adaptability, and efficiency. Below are the core advantages organizations can expect from deploying agentic AI systems in security contexts.
Self-direction
One of the most powerful features of agentic AI is its ability to act without waiting for human prompts. Instead of running predefined playbooks or waiting for analyst approval, these systems can recognize a threat or opportunity and pursue it with purpose.
Contextual awareness
Agentic systems aren’t just rule-followers, they’re situationally aware. They analyze context dynamically, taking into account the environment, threat patterns, asset sensitivity, and even user behavior. For instance, they might treat a failed login differently if it’s coming from a known IP versus an unfamiliar one, or escalate their response based on the criticality of the asset involved.
Long-term task execution
Cybersecurity tasks aren’t always quick fixes. Some threats emerge gradually, or require weeks of monitoring, correlation, and response. Agentic AI excels at these long-haul efforts. It can pursue an investigation across days or weeks, tracking evolving signals and continuing to act on new information.
Faster, more adaptive defense
Because agentic AI can perceive, decide, and act in tight loops, it reacts more quickly to evolving threats. If an attacker changes tactics mid-campaign, the AI can adjust its response on the fly.
Risks and ethical considerations
While agentic AI offers exciting advantages in cybersecurity, it also raises critical concerns around trust, safety, and control. Understanding the risks is essential to deploying them responsibly and effectively.
Alignment challenges
One of the most fundamental risks with agentic AI is ensuring that its goals align with human intent. Because these systems can act autonomously and pursue objectives over time, even small misalignments between their goals and the organization’s security priorities can lead to harmful outcomes.
Unintended behavior and safety risks
Agentic AI systems often operate in complex, dynamic environments where it’s impossible to foresee every situation they’ll encounter. As a result, they may behave in unexpected or unsafe ways. A well-intentioned AI might isolate a business-critical system at the wrong time, delete logs that were needed for a forensic investigation, or escalate a response too aggressively.
Transparency and control
Another key concern is the ability to understand and control what agentic AI systems are doing. Their decision-making processes can be opaque, and this lack of transparency makes it difficult for security teams to audit actions, explain outcomes, or confidently intervene when something goes wrong. Building agentic systems with clear logging, explainability features, and human-in-the-loop (HITL) override mechanisms is essential.
Agentic AI vs. autonomous agents
The terms “agentic AI” and “autonomous agents” are often used interchangeably – but they’re not always the same thing. While they share similarities, understanding where they overlap (and where they don’t) is key to grasping how different AI systems behave in cybersecurity contexts.
What they have in common
- Autonomy: Both operate without contrasting human input, making decisions and taking action based on their understanding of the environment.
- Goal-directed behavior: Each is designed to pursue outcomes rather execute isolated tasks.
- Continuous feedback loops: Both use cycles of perception, planning, and action to adapt to changing circumstances.
How they differ
- Architectural complexity: Agentic AI is often a broader conceptual framework. It may include one or more autonomous agents, plus memory, long-term planning, and meta-reasoning capabilities. In contrast, an autonomous agent typically refers to a single, self-contained actor.
- Scope of behavior: Autonomous agents are usually built for narrower, well-defined tasks (e.g., scan logs and quarantine infected hosts). Agentic AI may coordinate multiple goals or agents, switching tasks and reprioritizing as the situation evolves.
- Planning and reasoning: Agentic AI tends to emphasize long-term planning, reflective reasoning, and dynamic goal management. Autonomous agents may follow more rigid procedures or predefined behaviors unless explicitly designed to adapt.
Future of agentic AI
In the near term, we can expect to see increasingly sophisticated agentic systems emerge across cybersecurity operations. These agents won’t just detect and respond to threats – they’ll proactively defend infrastructure, predict risks before they manifest, and manage security posture as a continuous, autonomous process. Many organizations are already experimenting with multi-agent frameworks, where several agentic AIs collaborate or compete to improve outcomes in dynamic environments.
Agentic AI also raises big questions about the future of artificial general intelligence (AGI). While today’s agentic systems are still narrow in scope, their ability to plan, reason, and act across time and context makes them an important stepping stone toward more general capabilities, making it critical to ensure safety, alignment, and compliance. But if developed responsibly, these systems could become essential allies in the digital world, protecting infrastructure and enhancing cyber resilience.