Understanding the Capabilities and Implications of Agentic AI in Surveillance Systems - Ahmed, Nisher; Hossain, Md Emran; Hossain, Zakir; Kabir, Md Farhad; Hossain, Iffat Sania (Indonesian Journal of Advanced Research, 2025)
Agentic AI can also mean the speeding up of new levels of surveillance systems the size of which has never been encountered before which grants automation decision making and realtime responses. Examples of stateful models of an agent are agentic AI, where an agentic AI isn't just a static function, but has capabilities to reason and learn with reference to the environment and goals. The paper explores the possible implications of embedding Agentic AI in surveillance systems, demonstrating how it could revolutionize monitoring, identification of threats, and response systems. Agentic AI: how this would take surveillance to a whole new levelThis is better than never breaking your objectivity at all, but leaves you to micromanage thousands of processes each requiring situational analysis and prediction in real time to create data packets, imploding the challenges of complex environments into just a number/sensible statistic. Yet, the application of Agentic AI for surveillance raises several ethical, legal, and social issues, such as about privacy, accountability, and the risk of misuse. It also outlines challenges to transparency, neutrality and oversight of Agentic AI systems (in their design and deployment), and emphasises the need for powerful regulatory frameworks able to confront risk. Moving forward the ability of Agentic AI to enhance surveillance technologies is significant, but the implementation of such a technology must be implemented correctly while remaining at or below the existing moral paradigms of our society and protecting the fundamental human rights of the individual.