We’ve spent years teaching machines how to follow instructions. Now, they’re beginning to understand purpose.

Agentic AI represents a new class of systems that can reason, collaborate and act independently toward defined goals—marking a fundamental shift in artificial intelligence. Instead of waiting for human prompts, these agents decide what to do next. They operate with context, not just computation.

For cybersecurity, where milliseconds matter, that evolution changes the game. The industry has relied on automation to handle scale. Agentic AI introduces autonomy—to handle complexity.

Most AI in cybersecurity still follows a familiar pattern: detect an anomaly, trigger a playbook, repeat. It’s efficient, but not intelligent. It doesn’t know why something is happening—only that it fits a rule.

Agentic AI changes that by reasoning through data. It doesn’t just flag suspicious behavior; it investigates the cause, correlates it with historical patterns, infers intent and proposes actions. It’s the difference between a machine that reacts and one that reflects.

SIEM gave us visibility. SOAR added orchestration. Generative AI brought natural-language interpretation. Agentic AI combines all of it—and adds reasoning.

The volume and velocity of modern threats have outpaced human capacity. Attackers already use AI to probe for weaknesses, craft phishing campaigns and disguise intrusion attempts at a scale no SOC can match manually.

You can’t fight AI-driven attacks with static rules and human reflexes. You need machines that think—systems that can infer, contextualize and adapt in real time.

This is cognitive defense: augmenting human judgment with AI that understands relationships, causality and consequence. Most organizations aren’t looking to replace human analysts—they just want to give them an army of digital colleagues who can reason at machine speed.

A recent example of this shift is Dataminr’s launch of Intel Agents for the physical world, which extends the company’s real-time event and threat intelligence platform into agentic AI territory.

Founder and CEO Ted Bailey told me the goal isn’t just faster detection—it’s deeper comprehension. “We’re not only discovering events faster than any other source,” he said, “but adding all of the context around them that customers need to understand and respond.”

Intel Agents operate as autonomous digital analysts. They continuously ask and answer hundreds of questions about every detected event, scanning millions of public data sources and historical archives to determine why something is happening and what it means.

Bailey described it as a leap from information to insight: “Agentic AI doesn’t need to do something superhuman—it just needs to operate at a superhuman scale.”

For cybersecurity, that principle translates directly. Imagine a SOC equipped with AI agents that don’t just flag suspicious traffic—they contextualize it, correlate it across identities, devices and geographies, and present an actionable summary before an analyst even logs in. That’s where agentic AI begins to fulfill what SIEM and SOAR only promised.

One of the more interesting aspects of Dataminr’s approach is architectural. Rather than relying on a single massive model, the company uses a network of smaller, domain-specific AI models—each one specialized and synchronized into what Bailey calls a “compounding system.”

That modular design mirrors how human teams operate: specialized experts working together toward a shared objective. In AI terms, it also reduces hallucination risk, improves efficiency and keeps reasoning grounded in the data most relevant to each task.

It’s a practical vision of how agentic AI should evolve—collaborative, distributed and scalable.

Of course, autonomy raises the same question that’s shadowed every major advance in AI: how much control should we give the machine?

I often joke (mostly) that I grew up on movies like “War Games” and “Terminator,” and I haven’t forgotten how those stories end. But the real risk isn’t giving AI too much power—it’s hesitating while attackers don’t. The key is balance: building frameworks where autonomous systems can reason and act, but always with human oversight, transparency and interpretability.

Den Jones, founder and CEO of 909Cyber, told me that balance is already breaking in many companies: “Most companies are unaware of the full extent of AI usage in their organizations. We’ve been fielding calls from concerned CEOs and other C-suite leaders trying to get control of AI usage—often uncovering AI agents already out of control. We’re facing a shadow AI crisis.”

His warning underscores the other side of the agentic AI equation: autonomy without governance quickly becomes chaos. Understanding what AI systems are doing—and what data they’re touching—must become a core security discipline, not an afterthought.

Agentic AI doesn’t just change what machines do; it changes how they think. It moves us from task execution to goal pursuit—from reaction to reasoning.

And that shift reaches far beyond cybersecurity. It will transform how we respond to emergencies, manage logistics, or even govern societies built on data. Context is no longer a human advantage—it’s becoming a computational one.