Summary
- AI agents follow predefined rules to complete tasks, while agentic AI operates independently, making real-time decisions and taking action without human input.
- In security, businesses using only generative AI agents miss the opportunity to detect, validate, and prevent threats before they escalate.
- Developing AI agent skills allows companies to integrate autonomous AI agents into their security systems, improving efficiency while reducing costs.
AI is everywhere, but not all AI is created equal. Two commonly used terms “AI agents” and “agentic AI” often get confused because they sound so similar. But these two phrases represent entirely different approaches to automation and decision-making.
Some AI systems follow scripts, while others can “think” and act on their own. That’s the difference between AI agents and agentic AI—one executes tasks based on predefined rules, the other makes real-time decisions and takes action without direct human input.
Knowing the difference between these two models is essential, especially in security, where the ability to think, adapt, and respond in real time can mean the difference between preventing a threat and reacting too late.
What Are AI Agents?
AI agents are software-based systems designed to perform specific tasks by following preprogrammed rules and instructions. These agents function like assistants, operating based on rules, logic, or algorithms they receive from users or other systems. In other words, they receive inputs, process them, and execute actions accordingly.
When Would I Use AI Agents?
Because AI agents rely on structured inputs, their functionality is often task-based. They can act on set conditions and follow specific rules, but they can’t adapt to new scenarios without manual updates. They also can’t operate without a human input or trigger.
Common examples of AI agents include:
- Chatbots that answer customers’ frequently asked questions.
- Virtual assistants like Siri or Alexa, which respond to voice commands.
- Generative AI agents, like ChatGPT or DALL·E, that create text or images based on prompts.
While these AI-backed assistants excel at streamlining tasks to make our lives easier, they ultimately can’t think autonomously or make strategic decisions on their own.
Here's how LVT is applying agentic AI:
What Is Agentic AI?
AI agents assist with specific tasks and stick to pre-set rules, but agentic AI operates independently, making real-time decisions and taking action without waiting for human commands. That’s what makes it so powerful in physical security, where quick, smart decision-making isn’t just a “nice to have”—it’s essential. Instead of reacting to what’s happening (like traditional security), agentic AI stays ahead of the game, continually learning and adapting as situations change.
When Would I Use Agentic AI?
Agentic AI works like a smart security system that doesn’t just watch, it thinks and reacts. It gathers information from cameras, sensors, and data feeds, constantly analyzing what’s happening in real time. Instead of just collecting data, agentic AI determines if something needs to be done (like triggering an alarm, issuing a warning, or notifying authorities) and takes action automatically.
What Does Agentic AI Mean for Security?
Today’s security requires more than just basic AI assistance, it needs autonomous AI agents that can detect, validate, and deter threats in real time.
Traditional AI agents, such as generative AI agents, can help analyze security footage or compile reports after an event occurs, but they don’t actively prevent incidents. Agentic AI, on the other hand, reacts instantly to threats—issuing deterrence warnings, alerting law enforcement, and even adapting its responses based on criminal behavior.
Steve Lindsey, Chief Technology Officer of LiveView Technologies, emphasized this critical difference: “Deterrence is only effective if threat actors believe they'll get caught. The problem with static cameras and binary reactions (like motion lights) is people are desensitized to it. The majority of deterrence can be done by a machine, but you've got to make it believable. It has to be interactive with what it's detecting.”
The more agentic AI is exposed to its environment, the more it learns and remembers. “Agentic AI gets smarter because now it knows the situation and it knows how to react based on what it’s seeing,” explained Lindsey. “And it’s going to react differently every time so bad actors know there actually is someone monitoring them. By learning patterns in behavior rather than focusing on identity, you can successfully detect, validate, and deter without hassling your good people.”
The Role of AI Agent Skills in Using Agentic AI
Successfully implementing agentic AI requires developing AI agent skills like:
- Training AI models to spot real security threats as they happen.
- Setting up AI systems to respond appropriately based on the level of danger.
- Blending AI-driven deterrence and automation with a current security setup.
Businesses that master the skill for using AI agent technology will stay ahead of the curve, better protecting their facilities, customers, and employees for less money.
The Shift from AI Assistance to AI Autonomy
The big difference between AI agents and agentic AI comes down to one thing: autonomy. AI agents are great at handling structured tasks, but agentic AI thinks for itself, makes decisions, and acts in real time.
When it comes to security, that’s the distinction that matters. If companies are using only generative AI agents to analyze information, they’re missing out on the ability to detect, validate, and prevent threats before they happen.
By building AI agent skills, businesses can integrate autonomous AI agents into their security systems, making protection faster, smarter, and more effective. The future of AI isn’t just about crunching data—it’s about taking the right action at the right time, every time.
Want to see how agentic AI can revolutionize your security strategy? Contact LVT today to schedule a demo.
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