What Makes Agentic AI Actually Agentic?

Agentic AI has quickly become one of the most talked-about terms in cybersecurity in 2025. Vendors like Microsoft, Reliaquest, and Deepwatch have all introduced their own takes on it — but is it more than just marketing? As interest grows, it’s important to define what separates truly agentic AI from basic automation or enhanced assistants. For those of us working on the technical frontier of AI for security operations, it’s critical we move beyond buzzwords and focus on function. Below are five core characteristics that make Agentic AI genuinely agentic.

1. Autonomous Task Execution (Not Just Automation)

Agentic AI goes far beyond simple rule-based or supervised learning models. It performs end-to-end tasks without explicit human instruction for every step. This includes:

• Gathering data from multiple systems (e.g., identity, endpoint, cloud, log sources)
• Formulating hypotheses based on contextual analysis
• Validating those hypotheses through active investigation
• Proposing or initiating actions based on conclusions

Whereas traditional AI often requires human prompting or operates within narrow, deterministic workflows, Agentic AI functions more like a junior analyst capable of taking initiative. It’s goal-directed and situationally aware.

2. Multi-Agent Collaboration and Role Specialization

One defining feature of advanced Agentic AI is the use of multiple specialized agents collaborating toward a shared outcome. Rather than a monolithic model, agentic systems often break the investigative process into discrete roles:

• Data collection agents
• Correlation agents
• Risk scoring agents
• Narrative summarizers

Each agent performs its role independently but contributes to a collective investigation process. This mirrors a human SOC team structure, where division of labor allows for more sophisticated and scalable analysis.

3. Contextual Reasoning and Hypothesis Validation

Agentic AI doesn’t just detect patterns—it reasons about them. For example, when investigating a brute force login attempt, an agentic system doesn’t just identify failed logins. It evaluates whether the pattern fits an actual attack or a misconfigured application by cross-referencing historical behavior, IP reputation, geo-location, and authentication methods.

This reasoning is hypothesis-driven: Agentic AI asks itself, “What might explain this signal? What data do I need to confirm or disprove that explanation?” This approach moves investigations from reactive alert review to proactive discovery.

4. Continuous Learning and Feedback Loops

Many systems claiming to be AI-powered are static once deployed. True Agentic AI is built toevolve. It captures analyst feedback, successful (or failed) actions, and new threat intelligence to refine future behavior.

This includes both supervised (human-in-the-loop) reinforcement and unsupervised pattern adaptation. Critically, our Agentic AI systems improve over time by capturing implicit feedback—such as observing how analysts investigate and respond to incidents—not just explicit input on AI-generated outputs. This means the system learns organically from real-world defender behavior. As a result, Agentic AI doesn’t rely on one-size-fits-all logic. It continually personalizes its reasoning to each customer’s environment, adapting to the specific configurations, user behavior, and threat landscape. This contextualization is what makes Ontinue’s approach truly unique: the AI learns from and for your environment—enabling smarter, faster, and more accurate decisions without constant manual retraining. .

5. Human Collaboration Without Human Dependence

Perhaps most important, Agentic AI doesn’t seek to replace human defenders — it amplifies them. It handles the repetitive and data-heavy parts of investigations so that humans can focus on judgment, escalation, and resolution.

But unlike older automation, it doesn’t depend on humans to fill in context gaps or interpret vague alerts. When the AI hands an incident to a human analyst, it brings with it:

• Full investigation steps
• Articulated reasoning
• Confidence levels and caveats
• Next-best action suggestions

This minimizes cognitive overhead and accelerates resolution.

The Future of Agentic AI: More Than a Marketing Label

Agentic AI isn’t a feature; it’s a framework for how we build intelligent, adaptable, and collaborative systems. In security operations, this approach allows us to finally address the scale problem that has plagued traditional MDR: too many alerts, not enough analysts.

By enabling machines to reason, collaborate, and act independently, we can reduce time to investigate, resolve more threats without human bottlenecks, and provide defenders with deeper, real-time insights.

We’re at the beginning of a new chapter in SecOps — one where AI isn’t just reactive, but truly agentic.

Stay tuned for our next blog post in our Agentic AI series, Building Trust in AI: How Agentic AI is Transforming SecOps.