In the world of physical security, change is a constant. From analog cameras to IP surveillance and now the rapid advance of artificial intelligence, the tools and technologies that professionals rely on have continually evolved.
The latest paradigm shift - ushered in by AI agents - promises to transform traditional video management systems (VMS) from passive video recorders into dynamic, decision-making platforms that act autonomously to detect and respond to threats.
The shift is subtle in language but seismic in implication. VMS platforms are no longer just about collecting footage - they’re becoming intelligent systems that can comprehend their environments, learn from interactions, and even orchestrate responses. In short, they’re becoming operational partners.
The agentic evolution
“Traditional video management systems are transforming from passive recording tools into intelligent platforms that actively participate in security operations,” said Rahul Yadav, Chief Technology Officer at Milestone Systems. “That’s mostly due to AI technologies. The Era of Agentics is changing VMS platforms from simply storing video to understanding and acting upon data in real-time.”
Unlike conventional AI models or video analytics that follow predetermined rules - detecting motion, identifying objects, or sending alerts - AI agents function more like autonomous assistants. They perceive context, make decisions based on evolving scenarios, and coordinate complex actions. This introduces a new layer of intelligence to security systems that were once reactive at best.
“Unlike traditional AI systems that follow prescribed steps, AI agents are autonomous systems capable of understanding contexts, making decisions, and taking actions independently,” Yadav added. “These systems don't just alert operators to potential issues but coordinate comprehensive responses.”
This could mean not only alerting a control room operator to a potential intrusion but also locking nearby doors, notifying local law enforcement, and pulling up live camera feeds from the incident location - all without human intervention.
Learning and adapting like a human
One of the most promising aspects of AI agents is their ability to learn over time, mimicking the experience curve of human operators. The more they operate in a specific environment, the better they get at recognizing anomalies, identifying false positives, and predicting events before they unfold.
“They'll identify threats, coordinate responses, and even predict incidents before they occur,” said Yadav. “This creates more intelligent security environments and provides data for security staff to act upon.”
The ability to predict and prevent, rather than merely react, has long been the holy grail of surveillance and access control. AI agents bring this capability closer to reality by ingesting vast quantities of data and continuously refining their understanding of what constitutes ‘normal’ and ‘suspicious’ behavior in a given context.
An adaptive layer over existing infrastructure
Florian Matusek, Director of AI Strategy and Managing Director of Genetec Vienna, shares a similarly optimistic view but tempers it with realism. He describes AI agents as the next logical step in AI’s evolution - but emphasizes that the technology is still nascent.
“AI agents are the next evolution in the rapidly developing AI landscape,” Matusek said. “They bring the potential to automate more processes and tasks than before by dynamically reacting to a given situation.”
Traditional VMS systems rely on fixed rule-based logic - motion triggers, schedules, and operator inputs. AI agents, by contrast, can adapt dynamically. For example, an AI agent monitoring a parking garage might react differently to an unfamiliar car parked in a restricted area at 2 PM versus 2 AM, taking into account time, location, past data, and contextual cues.
“Compared to rigid rules, AI agents have the potential to be more adaptive,” Matusek explained. “However, it should be noted that agents are still a very young technology, and we have yet to see actual applications.”
Promise meets caution
While the vision is compelling, both Yadav and Matusek acknowledge that the deployment of AI agents in security is still in early stages. For many integrators and end users, questions remain about cost, compatibility, privacy, and control.
Security teams operate in high-stakes environments. The idea of handing over control - even partially - to autonomous systems introduces concerns about reliability and explainability. What if an AI agent locks down a facility due to a false positive? What if its “understanding” of a context leads to the wrong decision?
These challenges point to the need for a “human-in-the-loop” model - where AI agents augment, rather than replace, human decision-making. In this hybrid model, AI agents could take over routine tasks or provide suggested actions, but operators remain ultimately in control.
The road ahead for integrators
For systems integrators, the rise of AI agents opens new possibilities - and new responsibilities. The ability to offer customers intelligent VMS platforms that go beyond simple recording and retrieval is a powerful value proposition.
But it also demands a deeper understanding of AI models, training data, and ongoing performance evaluation. Integrators will need to work closely with VMS vendors and AI solution providers to ensure these systems are not only well-calibrated but also transparent and ethically designed.
As regulations around AI transparency and accountability tighten - especially in regions like the EU and parts of Asia - this collaboration will be critical.
Moreover, integrators must guide clients through change management. Moving from a traditional VMS setup to an AI-agent-powered environment may involve workflow redesigns, operator retraining, and new cybersecurity protocols.
A glimpse into the future
Despite the early-stage nature of AI agents, there is a growing consensus that this is not just a technological trend - it is a fundamental shift in how surveillance and access control systems will operate.
We’re just scratching the surface. The potential to create autonomous, self-learning security ecosystems that continuously evolve is enormous. It’s not science fiction anymore.
We’ll start to see more use cases emerge in the coming years, especially in sectors like critical infrastructure, transportation, and smart cities. But it will require experimentation, iteration, and trust.
In essence, the transition from static to dynamic, from reactive to proactive, is underway. The VMS of tomorrow is not just a database of videos - it is a thinking, learning entity that partners with humans to create safer environments.