Agentic AI vs Traditional Surveillance: A New Era in Physical Security Solutions

‍SUMMARY 

• Traditional video analytics detect and classify motion or objects but rely on humans to interpret and respond, leaving security teams overwhelmed by alerts.
• Agentic AI combines machine learning, large language models, and sensor fusion to perceive context, reason about risk, and autonomously take action.
• Real-time responses include tracking intruders, activating spotlights, and issuing personalized voice warnings—creating the impression of live human oversight.
• Outcome: reduced false positives, faster responses, scalable coverage, and stronger security for large, complex sites.

For decades, security cameras were essentially electronic eyes that recorded footage for human review but were unable to act on what they saw. Traditional video analytics, introduced in the early 2000s, marked a leap forward by detecting motion, classifying objects, and triggering alerts when set rules were broken. But even then, the system’s role ended with a notification. It was still up to a human to respond. 

LVT’s use of agentic AI changes that equation. By combining machine learning, large language models, and multi-sensor integration, our systems not only detect activity, they interpret it, decide on the best course of action, and respond in real time. The result is scalable, round-the-clock coverage that frees up human teams to focus on the threats that require their attention. 

From Smart to Independent

Traditional analytics interpret visual information using algorithms trained to detect changes in a scene. They label objects—such as a person, vehicle, or animal—and apply simple rules, such as triggering an alert if a person crosses a virtual tripwire or loiters too long in a zone. This reduces the need for constant monitoring, but the process remains reactive. The system detects, then humans make a decision. 

Common rule-based analytics include detecting perimeter breaches, flagging prohibited parking, or identifying unattended objects. While effective, these systems operate within fixed logic. They can’t determine why something is happening or whether it truly poses a threat.

On the Edge

Analytics can run directly in a camera or on an “edge” device, such as a network video recorder. Processing video at the source reduces bandwidth use and latency, making it faster to deliver alerts to a video management system. But regardless of where the processing happens, traditional analytics remain bound by pre-set rules. They can spot motion at midnight, but they can’t decide if it’s suspicious, harmless, or worth escalating. That decision still falls to a person.

What Makes Agentic AI Different

Agentic AI represents the next chapter in the evolution of security cameras. Where traditional analytics rely on fixed rules, agentic AI uses a goal-oriented framework to interpret a video scene and determine the best course of action. LVT’s systems operate in a continuous loop of three core functions:

• Perception: The system takes in visual and sensor data with nuance. Not only can it detect unwanted guests, it can interpret behavior, such as pacing, loitering, approaching from unusual angles, or interacting with equipment.
• Reasoning: It compares what it sees to learned patterns, risk models, and mission goals. For example: This person is approaching a restricted access point from the rear at 2 a.m. That deviates from known safe behavior.
• Action: Based on its analysis, it takes next steps, like tracking a subject with a PTZ camera, activating a spotlight, or issuing a personalized audio warning.

Sensor Fusion and Autonomy

Agentic AI is not limitless. It still operates within defined parameters, but it doesn't require micromanagement. It’s the difference between a system that flags a problem and one that sets out to solve it.

Many security units integrate more than optical HD cameras. They can draw data from thermal imagers, automatic license plate recognition (ALPR) cameras, and audio sensors into a single platform. This process, known as sensor fusion, gives the system better situational awareness and improves accuracy in all lighting and weather conditions.

Securing Large Outdoor Properties

Consider a large equipment yard or utility substation in a rural area. In a traditional setup, video cameras with analytics will monitor the perimeter and detect motion or unauthorized entry. If someone crosses a digital tripwire, for example, the system sends an alert to a remote monitoring station.

A human operator logs in, reviews the footage, and decides whether to act. They might use the system’s two-way audio to tell the trespasser to leave, or determine it’s a harmless event, such as a delivery driver turning around. Either way, the process requires human attention and intervention.

Now, picture the same scenario with an LVT mobile unit equipped with agentic AI. Instead of flagging motion, the system interprets behavior. It can recognize a person approaching from an unusual direction after hours or lingering near equipment. The AI can autonomously pan and zoom to track the individual, activate a spotlight, and issue a tailored voice warning, such as, "You in the green jacket. This is private property. Step away from the fence!" 

The specific details create the impression that a live guard is watching in real time, often scaring away perpetrators before the incident escalates. No operator is burdened. The event is logged, classified, and stored for later review, allowing human teams to stay focused on higher-priority tasks.

Why This Matters

For organizations with large, complex sites, the volume of alerts from traditional systems can overwhelm security teams. Every potential incident competes for attention, creating delays and draining resources. LVT’s agentic AI closes that gap. 

By interpreting context, adapting to evolving patterns, and responding autonomously, our systems filter out false positives and address lower-level issues before they reach an operator. That means faster responses, fewer unnecessary calls, and more time for human teams to handle genuine threats. The outcome is measurable improvement in site security, not to mention the peace of mind that comes from knowing your system is already taking action.

Evolution, Not Replacement

Traditional analytics remain a cornerstone of modern security. They are proven, dependable, and highly effective for highlighting activity. But as demand grows for scalable, proactive coverage, rule-based systems alone can’t keep pace.

LVT’s agentic AI builds on the strengths of traditional analytics while expanding their capabilities. Want to learn more? Request a demo today.