Architecting AI Gateways: Proxying Agentic Workflows and MCP Traffic
Architecting AI Gateways: Proxying Agentic Workflows and MCP Traffic Traditional API gateways break down when autonomous agents initiate 50 cascading tool calls at once. Here is how to deploy AI-native reverse proxies to cache reasoning chains, route MCP traffic, and throttle rogue agents — and why the security story has become significantly more complicated than the original gateway pitch anticipated. By 2026, the AI landscape has definitively shifted from static prompt-response chatbots to autonomous, multi-step agentic workflows. Large language models now act as reasoning engines that independently query databases, trigger external APIs, and execute complex code. This architectural leap has exposed a critical flaw in traditional enterprise network infrastructure: legacy API gateways were designed for linear, predictable, 1:1 request-and-response REST traffic. They are entirely unequipped to handle the erratic, high-volume, token-heavy traffic generated by autonomous AI agen...
