What is the Difference Between AI Agents and Agentic Systems in Security Operations?
In security operations, AI agents execute specific SOC tasks. Agentic Systems own outcomes across the SOC end-to-end. Both solve real security problems, but they address different operational needs.
How Do AI Agents and Agentic Systems Differ?
A Detailed Comparison for Security Operations
Characteristic | AI Agents | Agentic Systems |
|---|---|---|
Goal | Perform a single predefined task | Achieve security objectives end-to-end |
Responsibility | Execute one function within a larger workflow automatically | Orchestrate functions, tools, and AI agents across workflows to deliver defined security outcomes |
Scope of Control | Localized to a task, rule, or tool | Spans domains, tools, and workflows |
Decision Authority | Logic-driven with human guidance | Makes decisions based on evidence within defined guardrails |
Context Retention | Context applies only to current task, then resets | Context persists across related alerts, incidents, and investigations |
Change Adaptability | Requires manual updates to rules or workflows | Adjusts strategy with new evidence and executes appropriate action |
Coordination Model | Manual or externally orchestrated | Native coordination across platforms, agents, and tools |
Human Involvement | Teams coordinate tasks and interpret outputs | Teams validate outcomes, manage exceptions, and guide strategy |
Strategic Impact | Improves efficiency on individual tasks | Enables consistent, scalable security operations |
AI Agents vs. Agentic Systems: Real-World SOC Use Cases
The difference between agents and agentic systems becomes clearest when the goal is not a single task, but a complete security outcome.
Use Case | With an AI Agent | With an Agentic System |
|---|---|---|
Alert Investigation | One agent enriches indicators or retrieves related threat data. | The system orchestrates enrichment, correlation, and analysis steps to complete the investigation and deliver an outcome-ready assessment. |
Detection Engineering | One agent translates detection logic into query syntax for a single tool. | The system orchestrates translation, testing, and evaluation across tools to maintain detection quality as environments change. |
Threat Hunting | One agent assists with query creation based on a threat description. | The system executes hunts across integrated tools, correlates results, and surfaces prioritized findings aligned to the hunt objective. |
Risk Prioritization | One agent retrieves vulnerability data or CVSS scores. | The system correlates vulnerability data with asset context and threat intelligence to continuously prioritize risk as conditions change. |
Why Security Leaders Need AI Agents and Agentic Systems
Modern SOCs need both AI agents and agentic AI systems—but for different purposes.
Benefits of AI agents in a SOC | Benefits of agentic systems in a SOC |
|---|---|
Automating well-defined, bounded tasks (alert enrichment, report generation) Improving efficiency on repetitive SOC workflows Supporting specialized, narrow use cases | Orchestrating objective-level work (end-to-end investigations, maintain detection quality) Scaling security operations to expand team impact and capabilities Managing decisions that require context across multiple data sources |
Enable Outcome-Level Security Operations with the GreyMatter Agentic AI SOC Platform
Agentic systems orchestrate AI agents, detection tools, and response workflows within defined policy guardrails—enabling SOCs to shift from manually assembling task outputs to achieving defined security objectives.
ReliaQuest GreyMatter is the agentic AI SOC platform that integrates into the fabric of your SOC, giving agentic systems the connectivity they need to orchestrate AI SOC agents across the threat detection, containment, investigation, and response workflow.