What is an AI Gateway? How it governs LLMs and agents

An AI gateway is presented as a centralized governance layer for AI traffic that verifies identities, enforces access and data-protection policies, manages costs, routes requests, and records activity across model calls and agent interactions with business tools. Unlike conventional API gateways, it is designed to interpret AI-specific elements such as prompts, tokens, model selection, sensitive information, and tool-call parameters. As AI agents increasingly perform actions such as accessing email, updating CRM systems, querying databases, and filing tickets, gateways need agent-specific identities, delegated user permissions, per-tool authorization, and action-level audit logs. The text distinguishes AI gateways, which primarily govern application-to-model traffic, from MCP gateways, which control agents’ access to tools through the Model Context Protocol, while arguing that organizations using agents need coordinated controls for both. It recommends gateways when AI use expands across teams, providers, regulated data, or connected business systems, while noting limitations including bypassed traffic, availability dependencies, added latency, and the inability to eliminate unsafe model behavior or replace human oversight for high-stakes decisions.

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