Police departments and corporate security leaders often struggle with limited resources when addressing a dizzying range of safety concerns.

Although overall crime rates in categories including gun assault, robbery, and homicide have decreased, instances of violent crime have increased, and cities with rising crime rates may struggle to adapt their operations to address the new threats plaguing their communities. 

Real-time crime centers — typically characterized as a centralized hub for security personnel to monitor disparate security technologies and alert systems — have become integral to many public safety efforts, coordinating real-time incident response across disaster recovery, common crime, and mass-casualty incidents. 

While police departments and some organizations have invested significant resources toward RTCCs, their playbooks are quickly becoming obsolete as new agentic AI capabilities radically transform what’s possible with security. 

To help evolve your safety strategy, let’s examine why traditional RTCCs are increasingly falling behind in the modern threat landscape, how agentic AI will significantly improve security strategy success, and what it takes to accomplish this reality.

The Shortcomings of Legacy RTCCs

In the past two decades, thousands of new security technologies have been introduced to address common security gaps and streamline operations with powerful capabilities. 

The challenge, however, is that solutions have traditionally failed to effectively integrate with other security solutions — meaning teams must manually monitor disconnected dashboards, assess alerts, and stitch together insights from disparate sources.

Real-time crime centers and similar security operations centers were built as a hub for police departments and select organizational security teams to unify their disparate technologies and alerts into a “single pane of glass.” It’s common for RTCCs to connect dozens of solutions, spanning: 

• CCTV and video surveillance streams
• Gunshot detection systems
• Social media feeds
• License plate reading (LPR) technology
• Drone and body-worn camera feeds

As more technologies have been integrated into RTCCs, teams have encountered the same challenges they faced before: Too many alerts, an inefficient prioritization system, and too many notifications for humans to handle alone. 

Essentially, the noise has become too great to effectively address every incident that matters.

Agentic AI is the answer to this challenge. 

Technology has now advanced to a point where AI can effectively orchestrate time-intensive tasks and allow humans to focus their attention where and when it matters. Teams can also access tools that effectively deter events before they happen — reducing the overall volume of incidents that teams must address.

Achieving this reality, however, requires an entirely new approach to building a connected tech ecosystem. 

The Technology Behind Modern RTCCs

Agentic AI is a powerful form of AI that can orchestrate activities in ways indistinguishable from humans. Although the technology is in its infancy, it is evolving rapidly as teams learn how to best position their data and systems for AI.

The “single pane of glass” will move from a literal user interface interpretation to an agentically-centered systems platform interpretation. When positioned correctly, AI can access holistic, real-time insights across security technologies, implement features designed to deter unwanted behavior (such as issuing customized loudspeaker announcements or triggering alarm systems), and flag situations that require immediate human attention.

This foundation is built through the following elements:

• Centralized data aggregation: Agentic systems need access to all security tools and an organization’s complete dataset to effectively orchestrate activities. Data needs to be aggregated centrally in the cloud and not on on-prem systems, because a data cloud provides optimal security measures and the necessary composability to connect to all business systems.

• Agentic flow integrations: Agentic systems will require newly created standards for agentic flow, like model context protocol (MCP) servers and agent to agent (A2A) protocols, and integrations between systems will need to move from direct software integrations to agentic flow integrations.

• Deterrence-capable hardware and software: Each component of the system must work in tandem to leverage deterrence features to help prevent crime. Cameras alone provide a baseline deterrence; however, that effect is amplified by agentic AI when a camera system detects abnormal behavior, so the AI triggers a customized audio warning through a connected speaker. A drone as a first responder system is also effective for deterrence and situational awareness, combining drones with strategically positioned spotlights, audio speakers, and other technologies.

Preventing Crime Proactively

Public safety and security teams have long battled a widening range of risks with increasingly limited resources. Agentic AI is an essential force multiplier that is capable of warding off routine risks effectively — while giving teams full situational awareness of incidents that require their immediate action.