The State of Agentic AI in Threat Intelligence 

AI has evolved. In the past couple of years, AI tools have slowly established credibility within the security operations center, closing the gap between earlier, conceptual ideas of what's possible with AI and real, usable capabilities. 

There's no question that AI improves speed and scale many times over, but for security to be truly effective, you need nuance, intent, and judgment: uniquely human qualities. The future will be marked by collaboration between AI and people, by augmentation, not replacement. 

AI has not yet reached its full potential in the modern SOC. Far from it. Where once the market was experimenting with whether AI can perform specific security tasks, the debate has now shifted to “Can we trust AI to run larger parts of security operations?” 

From “Show Me the Proof” to “Production”

Short response? Yes, it can!  

Longer response? Yes, it can, but when done correctly and in the right hands.  

AI can analyze, decide, and act within set boundaries. Agentic AI is being used in SOCs now, because these systems are designed to go beyond just helping analysts, to operating as goal-driven agents embedded directly into security workflows. 

However, although lots of vendors can demonstrate agentic behavior in controlled settings, only a handful can operate agentic AI continuously in production environments. It's this gap between demo-level autonomy and safe, reliable operational autonomy in production that's become the real differentiator in buying decisions. This is what decides if platforms are experimental or truly enterprise-ready. 

Over the past 18 months, agentic AI has left the research lab behind and joined the SOC. It can now handle incident response and threat intelligence workflows. Top security platforms aren’t just using AI to generate summaries, recommendations, or insights; they are wiring it directly into the systems that trigger investigations, orchestrate response steps, and update controls. 

The platforms that matter can operationalize agentic AI capabilities safely.

Agent Sprawl and Cost Concerns 

Most analysts are worried about uncontrolled agent sprawl across their business, for good reason. If you have multiple agents operating without centralized oversight, the sprawl can create new attack surfaces or disrupt operations. Imagine dozens of new interns running around the office, all making their own decisions without checking in with each other or a supervisor. It will inevitably get to a point where someone finds themselves with three meetings scheduled at the same time, or the front door is left wide open. 

Another worry is cost predictability. Always-on agents can result in compute and licensing costs soaring. Analysts are also concerned about the problems many platforms face in terms of proving outcomes, impact, and ROI. Because of this, governance, observability, and AI controls are being seen as a new category of infrastructure, as a management layer is needed to keep all these agents from running amok.

Where the Value Shows Up  

Those who are getting it right are finding that agentic AI is delivering measurable value in high-volume alert triage and investigation workflows that once overwhelmed human analysts. It's being harnessed to inject intelligence into threat feeds, internal telemetry, and case data, so fragmented signals can be turned into coherent attack descriptions rather than isolated alerts. 

Having effective agentic systems helps make response decisions faster, but it still leaves people in control to handle high-risk actions that might not be easy to undo. It’s much like having a very competent junior analyst burning the midnight oil to join the dots and flag suspicious patterns. However, they still need a more senior analyst to give the go-ahead before doing something that affects business-critical systems.

Where Vendors Fall Short 

Some vendors have agents that can easily analyze and summarize, but cannot execute actions within operational systems. Others that rely on automation don’t have the contextual understanding, which can lead to bad or risky decisions. 

Black-box models that cannot explain why an action was taken are another persistent problem that leads to trust and compliance issues. Other platforms don’t have the means to simulate, test, constrain, or govern agent behavior before and during execution. No one would willingly give someone the keys to their car without checking if they have a valid driver's license and insurance, yet it’s pretty much what some vendors are proposing.

So, What is the Way Ahead? 

The agentic AI systems we can expect to see in the near future will be judged by quantifiable results rather than the number of actions taken. They will marry intelligence, decision-making, and execution; they won’t treat them as different layers. 

Vital elements, like policy enforcement, approval workflows, and kill switches, will become an expected part of agent design. The industry will want fewer agents, each owning their own end-to-end workflows; they will move away from armies of agents, each capable of only a narrow scope of work.

Intelligence as infrastructure 

Agentic systems cannot make good decisions without high-quality, accurate, unified threat intelligence. This includes context on old and new adversaries, infrastructure, and current vulnerabilities (and those just rearing their heads). They also need to understand organizational exposure, as it determines how agents will prioritize and act. 

Without the solid foundation that unified intelligence provides, agents will only amplify noise and generate low-confidence signals and will be utterly ineffective at limiting risk. In this way, intelligence becomes the layer that governs how agents will behave and simultaneously informs their execution.

What Cyware Has Done Differently  

Cyware has built agentic workflows on top of a unified threat intelligence foundation using its AI Fabric. This acts as an intelligence layer that ingests, normalizes, enriches, correlates, and scores threat data before agents act on it. Think of it like a new electric car. You could ditch the old transmission that everybody trusts and uses, or you could build on proven automotive engineering that people already rely on. 

With a foundation like Cyware’s, AI-driven reasoning and agentic workflows support investigations, recommend actions, automate routine tasks, and bring intelligence sharing to trusted partners through well-governed exchanges.  

Cyware’s AI Fabric, powered by Quarterback AI, exemplifies this approach. It combines GenAI, agentic workflows, automated reasoning, and guided investigations into a single operational layer that strengthens the full security stack. Companies can benefit from a security posture that continuously ingests and acts on intelligence, enabling faster decision-making, quicker team adaptation, and stronger resilience.

What this Means for CISOs in 2026 

CISOs don’t buy AI features in isolation anymore: they want real operational leverage. The hard decision they need to make is which parts of their cyber defense can be responsibly delegated to machines and what guardrails they need to apply to do this safely. 

Platforms that cannot demonstrate governance, explainability, and measurable outcomes will never slide past the scrutiny that enterprise procurement and regulators demand today.  

Agentic AI was never designed to replace all human controls fully, but to make cyber defense scalable in the face of surging volumes of threats and growing complexity. The ones that succeed will be those that can take threat intelligence and convert it into governed, coordinated activity, rather than producing yet another stream of alerts. 

This technology has moved past the experimental stage. Today, it's all about trust, governance, and proven execution in production environments where mistakes have real consequences. 

If you’d like to see how Quarterback AI can help, schedule a demo today.