Agentic AI

Agentic AI refers to AI systems that do not just answer a question but pursue a goal across many steps with little human input, deciding what to do next, taking an action, observing the result, and looping until they finish. Where a chatbot maps one prompt to one reply, an agent plans, uses tools, keeps memory, and recovers from errors on its own. That shift from "responding" to "acting" is the single most important idea in modern AI security, and it touches everyone working in cybersecurity today.

How Agentic AI Works

An agentic system is built from a few moving parts. A language model supplies the reasoning, a set of tools lets it touch the outside world (run a command, query an API, read a file), a memory store lets it carry context between steps, and a control loop re-prompts the model with each new result. This surrounding machinery is called agentic scaffolding, and it is what converts text predictions into real actions.

The loop is simple but powerful: the agent breaks a goal into sub-tasks, picks the next action, executes it, reads what came back, and updates its plan. Because it can branch and retry, an agent can complete workflows that a single prompt never could.

Why Agentic Scaffolding Matters for Security

The scaffolding, not the model alone, is where the security stakes live, because scaffolding is what grants the power to act. A model that can only write text is limited; the same model wired to a terminal, a network, and a credential store can do real damage. This is exactly why mapping agent behavior to frameworks like MITRE ATT&CK and tracking it through threat intelligence has become essential for defenders.

The clearest illustration is the GTG-1002 case. Reporting on how attackers are starting to weaponize agents, documented in our breakdown of how hackers use AI, described an operation that wired Claude Code onto a Kali Linux machine and exposed its offensive tools as MCP servers, letting the agent act largely on its own across reconnaissance, exploitation, and lateral movement. That setup scored the maximum assessed risk of 100, not because the underlying model was uniquely powerful, but because the scaffolding removed the human from nearly every step.

Defending in an Agentic World

The same capabilities that make agents dangerous to defenders also make them useful for defense: agents can triage alerts, enrich threat intelligence, and reproduce attacks at machine speed. The goal is not to ban agency but to constrain it. Scope each tool tightly, give the agent least-privilege credentials, log every action it takes, and require human approval before anything irreversible.

Agentic AI is not a future risk, it is the current shape of both attack and defense. The teams that understand the scaffolding, and decide deliberately where a human must stay in the loop, are the ones who will stay in control as agents take on more of the work.