Skip to main content
Who is this for? Security Engineers, CISOs, and Technical Decision-Makers
February 2026 · ContraForce Team · 6 min read The term “AI” gets applied to everything from simple automation to sophisticated reasoning systems. In security operations, understanding the difference matters — because the gap between a chatbot and an autonomous agent is the gap between answering questions and actually solving problems.

Copilots vs. Agents: A Critical Distinction

Most AI tools in security today are copilots — they assist humans but don’t act independently. A copilot might suggest a query, summarize an alert, or draft a response. But the human still makes decisions and executes actions. Agents are different. An agent:
  • Perceives its environment (incoming incidents, entity data, historical context)
  • Reasons about what to do (investigation steps, response actions)
  • Takes action autonomously (executes queries, runs playbooks, updates incidents)
  • Learns from outcomes (refines recommendations based on results)
Security Delivery Agents in ContraForce are true agents. They don’t wait for you to ask questions — they investigate incidents, gather context, and execute responses based on your configured policies.

Why Security Operations Needs Agents

Security operations has a structural problem: the volume of incidents exceeds human capacity to investigate them thoroughly. Most SOC teams operate in triage mode, quickly classifying alerts and hoping the important ones get attention. Copilots don’t solve this problem. They make individual analysts slightly faster, but they don’t change the fundamental math. You still need a human in the loop for every incident. Agents change the equation by handling incidents end-to-end:
CapabilityCopilotAgent
Analyze an incidentSuggests analysis stepsPerforms analysis automatically
Gather contextRecommends what to look upQueries sign-in logs, device timelines, related incidents
Determine responseSuggests actions to takeExecutes response actions through Gamebooks
Document findingsDrafts notes for reviewWrites incident comments automatically
Handle volumeOne incident at a timeProcesses incidents in parallel across workspaces

What “Agentic” Actually Means

The term “agentic AI” describes systems that act with autonomy toward goals. Key properties include: Goal-directed behavior — Agents work toward objectives (investigate this incident, contain this threat) rather than responding to individual prompts. Persistent context — Agents maintain awareness of the environment across multiple interactions, understanding how entities relate and how incidents connect. Tool use — Agents invoke external systems (query APIs, execute playbooks, update records) to accomplish their goals. Adaptive execution — Agents adjust their approach based on what they discover, rather than following rigid scripts. Security Delivery Agents exhibit all these properties. When an incident arrives, the agent determines what investigation is needed, gathers relevant context, decides on appropriate response actions, and executes them — all without human intervention for routine cases.

The Trust Question

Autonomous agents raise an obvious concern: how do you trust an AI to take security actions in production? ContraForce addresses this through progressive autonomy:
  1. Start manual — Run agent investigations on-demand and review every output
  2. Automate investigation — Let agents analyze incidents automatically, but review before action
  3. Enable response — Allow agents to execute Gamebooks, with confidence thresholds controlling when actions proceed
This phased approach lets you build confidence in agent behavior before granting broader autonomy. You also maintain human-in-the-loop controls for sensitive actions and comprehensive audit trails for everything agents do.

The Future of Security Operations

Agentic AI represents a fundamental shift in how security operations can work. Instead of humans doing repetitive investigation with AI assistance, agents handle routine work while humans focus on judgment calls, exception handling, and strategic improvements. This isn’t about replacing analysts. It’s about making analyst capacity go further — handling the incident volume that would otherwise be impossible to address thoroughly.

Quick Summary

  • Copilots assist humans; agents act autonomously toward goals
  • Security Delivery Agents investigate incidents, gather context, and execute responses
  • Agentic AI properties: goal-directed, persistent context, tool use, adaptive execution
  • Progressive autonomy lets you build trust before enabling full automation
  • Agents handle volume; humans handle judgment and strategy
Questions? Contact us at [email protected].