Every security vendor now claims to be "agentic." The word appears in pitch decks, product pages, and earnings calls with the same frequency that "AI-powered" did three years ago. If your B.S. detector is firing, that's healthy.
But here's the problem: dismissing "agentic" as marketing fluff means missing a genuine architectural shift—one that determines whether AI actually reduces your team's workload or just adds another interface to manage.
This post breaks down what "agentic" actually means, why most products using the term don't qualify, and how to evaluate whether a tool will help your SOC or just generate more noise.
Security teams have been automating tasks for years. SOAR playbooks, scheduled scripts, if-then-else workflows—these are automation. They execute predefined steps reliably. They don't adapt, reason, or handle edge cases. When something falls outside the playbook, they stop or fail silently.
Then came the assistant wave. ChatGPT launched, and suddenly every security product added a chat interface. Type a question, get an answer. These assistants—often called copilots—help you work faster. They translate natural language to KQL. They summarize alerts. They draft incident reports.
But assistants don't do the work. You still decide what to investigate. You still review every alert. You still determine whether something is a true positive. The assistant speeds up individual tasks while the overall workflow—and the human bottleneck at its center—remains unchanged.
Agents are different. An agent receives a goal, breaks it into subtasks, executes those subtasks (often by invoking tools or other agents), evaluates results, and adjusts its approach based on what it learns. An agent investigating a suspicious login doesn't just tell you what queries to run—it runs them, correlates the results, checks related systems, and delivers a verdict with supporting evidence.
The difference isn't semantic. It's operational:
Most products claiming "agentic" capabilities are assistants with better UX. They chat. They suggest. They draft. But when you close the laptop, they stop working.
SOC teams face roughly 3,800 alerts per day. Industry data suggests 62% go uninvestigated—not because analysts are lazy, but because there aren't enough hours. The backlog grows. The coverage gap widens.
Assistants don't solve this. They make individual analysts faster, but the queue still depends entirely on human throughput. If your team investigates 500 alerts per day with an assistant instead of 400, you've improved efficiency by 25%. And you are still ignoring 3,300 alerts.
Agents change the math. An agent that triages alerts autonomously—correlating related events, enriching with threat intel, determining true vs. false positive—handles the volume problem directly. The human reviews completed investigations rather than initiating them.
This is the difference between a tool that helps you type faster and a tool that reads your email and drafts responses you approve.
If agents are so capable, why not let them run everything?
Because security operations require both consistency and judgment—and those don't always coexist in the same system.
Some tasks demand deterministic execution. When you quarantine an endpoint, you need that action to execute the same way every time. When you collect forensic evidence, chain of custody depends on repeatable, auditable steps. Introducing AI "reasoning" into these workflows creates variance where you need reliability.
Other tasks require genuine reasoning. Determining whether a login anomaly represents credential theft or an employee traveling requires contextual judgment. Mapping an attack chain to MITRE ATT&CK requires understanding adversary behavior. Deciding which alerts deserve escalation requires weighing incomplete information.
The vendors promising fully autonomous SOCs gloss over this tension. Pure determinism can't handle novel situations. Pure autonomy can't guarantee consistency. Security operations need both.
Strike48 agents use a hybrid architecture that combines deterministic and cognitive execution within the same workflow.
Deterministic steps execute reliably and repeatably. Collect these log sources. Run this query. Apply this enrichment. Generate this hash. These steps produce consistent results regardless of which agent instance runs them.
Cognitive steps apply reasoning where judgment matters. Is this behavior anomalous given this user's history? Does this alert cluster represent a single incident or multiple unrelated events? What's the most likely root cause given these indicators? These steps leverage LLM capabilities for tasks that benefit from contextual understanding.
A single investigation might involve dozens of each. The agent doesn't "decide" when to reason and when to execute—the workflow architecture defines which steps require which approach.
Here's what that looks like in practice:
Alert Assessment Agent receives a cluster of 47 related alerts. Deterministic steps collect the relevant logs, normalize timestamps, and extract key fields. A cognitive step analyzes the pattern and determines this represents a single phishing campaign targeting the finance department. Deterministic steps package the evidence. A cognitive step generates the executive summary. The completed case routes to a human for review—not for investigation, but for decision.
Detection Engineering Agent receives new threat intelligence about a ransomware variant. Deterministic steps parse the IOCs and map them to available data sources. A cognitive step designs detection logic optimized for the organization's specific tech stack. Deterministic steps validate syntax and test against historical data. A cognitive step evaluates false positive likelihood based on environment characteristics. The proposed rule routes to a human for approval before production deployment.
This isn't "AI with guardrails." It's purpose-built architecture that applies the right approach to each step.
When vendors claim agentic capabilities, ask:
Does it work when humans aren't watching? If the product requires an analyst to initiate every action, it's an assistant regardless of branding. Agents operate on goals, not prompts.
Can it invoke tools and other agents? Real agents orchestrate complex workflows—calling APIs, querying data sources, handing off to specialized agents. If every capability lives in a single chat interface, you're looking at a sophisticated assistant.
How does it handle tasks requiring consistency? Ask about evidence collection, containment actions, compliance reporting. If the answer involves "AI reasoning" for tasks that demand repeatable execution, probe further.
What's the audit trail? Autonomous operation without explainability is a non-starter for security. You need to see what the agent did, why it did it, and what information informed each decision.
Where are the human approval gates? Agents that execute containment, isolation, or remediation without human authorization aren't mature—they're reckless. The goal is speed with control, not speed instead of control.
The gap between assistants and agents shows up in operational metrics.
With an assistant, MTTR improves because analysts work faster on each incident. Total incidents investigated stays roughly constant—bounded by headcount and hours.
With agents, total incidents investigated scales with compute. Analysts review completed work rather than performing it. The constraint shifts from "how fast can humans work" to "how many decisions require human judgment."
That's not a marketing distinction. It's a capacity model that determines whether your SOC can actually address the alert volume it faces.
See the difference for yourself. Request a demo to watch Strike48's hybrid agents investigate real alerts—deterministic execution where consistency matters, cognitive reasoning where judgment matters, human approval where accountability matters.