Two different ideas have been sharing one word
For the last few years, "AI in the SOC" has mostly meant a chat interface bolted onto a SIEM: ask a question about an alert, get a summarised answer, then do the actual investigation yourself. That is a real productivity gain, and plenty of teams have shipped it. It is also not what most people picture when they hear "autonomous security."
Agentic AI is a different shape of tool. Instead of answering a question you ask, an agent is given a job — investigate this alert, enrich this indicator, isolate this device if the evidence supports it — and it carries that job through to a conclusion on its own, consulting the same data sources a human analyst would, then acting or reporting depending on what it finds. The distinction matters because it changes who is doing the work of investigation, not just how fast they can look things up.
What actually changed to make this possible now
Three things had to be true at once for autonomous investigation to become practical rather than a research demo. Language models had to get reliably good at multi-step reasoning over structured and unstructured evidence together — reading a log line and a threat intel report and drawing a consistent conclusion from both. Tool-use had to mature to the point an agent could reliably query a SIEM, an EDR console, and a threat intel feed in the right order without a human supervising each call. And the economics had to work: running an agent through a full investigation, tens of times a day, at a cost that beats the fully-loaded cost of an analyst doing the same thing.
All three arrived close together, which is why the shift from "AI assistant" to "AI agent" in security operations has felt sudden rather than gradual. It was not sudden. It was three separate curves crossing a threshold at roughly the same time.
What this looks like in an actual SOC
urgentic runs eleven specialist agents across a single investigation — triage, entity enrichment, threat intelligence correlation, hunting, incident response, detection engineering, and more — each one doing a defined piece of the work an analyst would otherwise do by hand, in sequence, on every alert. The measured result across the product today is an investigation that completes in under three minutes, around the clock, without a human opening a ticket first.
The important word there is "every." A traditional SOC triages by volume — the loudest or most obviously severe alerts get looked at, and the rest queue or age out. An agentic pipeline does not get tired or fall behind, so the question stops being "which alerts deserve attention" and becomes "what did every alert actually turn out to be."
What this does not mean
It does not mean removing people from security operations. It means removing the repetitive, evidence-gathering part of investigation from the list of things a person has to do manually, so the humans on a team spend their time on judgement calls, escalation decisions, and the handful of genuinely ambiguous cases that benefit from experience rather than throughput.
It also does not mean every vendor claiming "agentic AI" in 2026 has built the same thing. The term is being used loosely across the industry right now, sometimes to describe systems that still require a human to approve every action, or that summarise rather than investigate. Worth asking any vendor, urgentic included: does the system reach a conclusion on its own, or does it hand you the evidence and wait for you to reach one? Those are different products, and only one of them changes the economics of running a SOC.