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What an AI gateway does
Your API gateway already authenticates every application, enforces rate limits and access policy, and logs each call. AI traffic needs the same treatment and usually does not get it. Model calls go straight from application code to a provider. Agents reach tools through MCP servers with shared keys. Nothing records which agent acted, for whom, or under what policy.
An AI gateway closes that gap. It routes model calls, MCP tool calls, and agent-to-agent traffic through one governed point that applies authentication, policy, guardrails, and quotas, and writes the record your risk team will ask for. For a primer, read what an AI gateway is and when you need one.
AI gateway vs LLM gateway vs MCP gateway
The names overlap in vendor marketing. What matters is which traffic passes through a governed point and which goes around it.
| Attribute | LLM gateway | MCP gateway | AI gateway |
|---|---|---|---|
| Traffic it sees | Calls to language models | Agent calls to tools through MCP servers | Model, MCP, and agent-to-agent traffic |
| Typical controls | Provider routing, quotas, cost, and prompt and response guardrails | Authentication, tool allow-lists, and per-tool policy | All of these under one policy model |
| When it is enough | Apps call models, but agents do not act on systems | Agents act on systems through a small set of tools | Agents call models and tools, and you need one evidence trail |
A record of every call
A decision log records what the agent was asked, what it called, what the gateway allowed or blocked, and what came back.
Stop an agent in seconds
A stop control revokes an agent's identity or blocks its traffic at the gateway in seconds, without a code change. Both controls are tested before go-live, not described in a runbook after the first incident.
Every question a reviewer asks, and the system that answers it
This table is what most pilots cannot fill in. Each examiner question needs a record, and each record needs a system that produces it as the agent runs.
| Question a reviewer asks | Record that answers it | System that produces it |
|---|---|---|
| Which agent took the action? | Agent identity on every call | Identity platform |
| On whose authority? | Delegation record linking agent and user | Identity platform and gateway |
| What did it touch? | Access log per model, tool, and API call | AI gateway |
| Under which policy? | Policy decision logged with each call | AI gateway |
| Was sensitive data exposed? | Guardrail results on prompts and responses | AI gateway guardrails |
| Can you stop it? | Revocation and block events, with test results | Identity platform and gateway |
The governance side of this, including the SR 26-2 evidence pack, is covered in AI governance consulting.
Build or buy an AI gateway
Building your own gateway looks cheap for the first model provider and the first agent. The cost arrives later: every new provider, protocol change, guardrail, and audit request lands on your team.
For a regulated firm we usually recommend running a platform gateway for the governed core and writing custom policy only where the platform cannot express it. If your API platform already includes AI gateway capability, start there.
Platforms we implement on
We implement AI gateway and agent controls on WSO2, MuleSoft, and Apigee.
WSO2 is where our practice goes deepest: the WSO2 AI Gateway and Agent ID can run, with their control plane, entirely inside your own infrastructure. See AI and agent governance on WSO2.
AI gateways, answered plainly.
AI gateways
Do we need an AI gateway if we already have an API gateway?
Often you can extend it. Many API platforms now include AI gateway capability. The test is whether model calls, MCP tool calls, and agent traffic actually pass through it with agent identity and policy applied. If they go around it, you do not have an AI gateway yet.
Is it worth building our own AI gateway?
Rarely for a regulated firm. A custom gateway leaves your team owning every provider integration, guardrail, and audit change. Use a platform gateway for the governed core and write custom policy only where the platform falls short.
Can an AI gateway run inside our own perimeter?
Yes. AI gateways and their control planes can run on-premises or in a private cloud, including air-gapped environments, so prompts, data, and logs stay where residency rules require.
How is an MCP gateway different from an API gateway?
An API gateway governs calls to APIs. An MCP gateway governs agents calling tools through MCP servers, which needs agent identity, tool-level allow-lists, and logging of each tool call. An AI gateway can do both under one policy model.
What audit evidence should an AI gateway produce?
For every call: the agent identity, the user it acted for, the model or tool called, the policy decision, guardrail results, and the response status. Plus revocation and block events for the stop control.
Working with us
Who implements AI gateways for banks?
Look for a partner that has implemented gateways and identity in regulated environments, works across more than one platform, and designs the evidence your risk team needs, not only the routing. Duczer East does this on WSO2, MuleSoft, and Apigee.
Which platforms does Duczer East implement AI gateways on?
WSO2, MuleSoft, and Apigee. WSO2 is our deepest practice, including the WSO2 AI Gateway and Agent ID.