Experts agree that AI agents are autonomous code modules capable of executing tasks independently. Cybersecurity researcher Andres Riancho from Wiz explains that a Large Language Model (LLM) can initiate actions via a Model Context Protocol (MCP) server, which connects AI models to external tools. Zafran Security’s CTO Ben Seri highlights the evolution of AI agents from generative AI, emphasizing their ability to act as analysts or mediators. Unlike traditional generative AI, these agents possess agency to perform tasks autonomously. However, the rise of agentic AI presents both risks and benefits. A significant concern is the potential for errors, known as “hallucinations,” which could lead to problematic outcomes. As AI technologies advance, ensuring trust, transparency, and a cautious approach remains essential for safe integration into various applications. Emphasizing these principles will help mitigate risks associated with AI agent deployment.
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