AI shopping agents are revolutionizing online purchasing by automating tasks such as browsing, price comparison, and completing transactions, leading to a significant increase in e-commerce traffic, as evidenced by DataDome's detection of nearly 1.2 billion requests from OpenAI crawlers in June 2025. This trend could potentially drive the US B2C retail market to generate up to $1 trillion in orchestrated revenue and globally between $3 trillion to $5 trillion, as per McKinsey research. However, the rise of agentic AI introduces new security challenges, as traditional security models based on static classifications are insufficient for these adaptable and autonomous agents. Modern security measures must focus on intent-based detection and trust-based security, differentiating access levels according to agent identity, behavior, and business relationships, to ensure legitimate AI agents are not blocked, thus preserving sales opportunities. Additionally, the introduction of Model Context Protocol (MCP) servers, which standardize AI agents' access to systems, necessitates robust authentication and behavior monitoring to prevent new attack vectors, emphasizing the need for businesses to develop adaptive security strategies as AI agent capabilities rapidly evolve.