Topic starter
06/09/2026 6:20 pm
TL;DR: AI gateways are converging around three jobs, model routing, MCP tool mediation, and broader agent communication, but the article’s key finding is that routing alone does not answer who is authorised to act, according to Aembit. That boundary is governed upstream by attested workload identity, represented-user context, and policy-driven credential issuance, not by the gateway label itself.
Editorial analysis by NHI Mgmt Group, based on content published by Aembit: “What Are the Main Types of AI Gateways? LLM, MCP, and Agent Gateways Explained”.
Key questions
Q: How should teams govern AI gateways that route model and tool traffic?
A: Teams should treat the gateway as the control boundary for identity, spend, logging, and policy enforcement.
Q: Why does gateway routing not answer who is allowed to act in agentic systems?
A: Routing decides where traffic goes, not whether the caller has delegated authority to take the action.
Q: Where do AI gateways fail if identity context is missing?
A: They fail at the point where enforcement depends on knowing more than the destination.
Practitioner guidance
- Separate routing from authority decisions Map which layer selects destinations, which layer evaluates policy, and which layer issues credentials so AI gateway labels do not blur control ownership.
- Require attested workload identity upstream Do not let a gateway enforce access unless the calling workload has been attested by an identity control plane before the request reaches the gateway.
- Preserve represented-user context Carry the human or organisational identity being represented through the request path so delegated actions remain accountable after routing and mediation.
Bottom line: AI gateways can centralise routing and observability, but they do not by themselves establish delegated authority for agent actions.
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06/09/2026 6:21 pm
Gateway terminology is obscuring a governance problem, not solving it. The industry is using one label for several different control points, but the decisive question is always authority, not routing. An LLM gateway, MCP gateway, or agent gateway can inspect and direct traffic, yet none of those labels tells you who is authorised to act. Practitioners should stop treating gateway category as a proxy for access control maturity.
A few things that frame the scale:
- Gartner predicts that AI systems will initiate 50% of all service requests by 2030, driven largely by agentic AI.
A question worth separating out:
Q: What is the difference between an MCP gateway and an LLM gateway?
A: An MCP gateway manages how agents connect to tools and data sources, while an LLM gateway manages how applications connect to the model itself. LLM gateways usually focus on routing, rate limiting, and token optimization. MCP gateways focus on tool discovery, authorization, tracing, and controlling what an agent can do once it reaches enterprise systems.
👉 Read our full editorial: AI gateways clarify routing, but identity still governs authority