AI agents in cybersecurity detect threats by analyzing large volumes of network traffic, system logs, and user behavior patterns. Machine learning models are trained on historical data to identify anomalies and detect potential security breaches. These agents use techniques like anomaly detection, intrusion detection systems (IDS), and behavior analysis to recognize suspicious activities. Once a threat is detected, AI agents can respond autonomously, isolating compromised systems, alerting administrators, or blocking harmful traffic. Over time, the agents improve by learning from new attack patterns, evolving to detect sophisticated threats like zero-day exploits or advanced persistent threats (APTs). The goal is to prevent breaches before they cause significant damage.