Every agent call goes through a front door. Make it a good one.

An AI agent gateway is infrastructure that sits between AI agents and everything they call, such as tools, other agents and internal APIs, and applies the same checks to every call before passing it on. It is the agent-era version of a familiar idea: one front door, so that security and limits live in one place instead of being copied into every service.

Agents make the front door matter more. An agent decides at run time which tool to call next, often in a loop. The Model Context Protocol gives agents a standard way to call tools, and the Agent2Agent protocol gives them a standard way to call each other ¹². Standard ways to call are good news. They also mean an agent can reach a great deal, quickly. The gateway is where a team decides how much of that reach to allow.

A model router picks who answers. An agent gateway decides whether the question may be asked.

An illustration. No bank, customer or system is depicted.

A bank's servicing agent is helping a branch officer answer a customer. It needs the customer's account summary, so it calls an MCP tool the bank has published behind the gateway.

First, who is calling. The agent presents its API key, and the gateway checks that the key is known, active and not expired.

Then, how much. The key's rate limit, the branch team's quota for the minute and the hour, and the token budget for this use case are checked. The budget has a hard cap: had this month's tokens been used up, the call would stop here, with the reason and the time to try again.

Then, what. A policy rule allows this tool only from the bank's own network and only in branch hours. Because the call is MCP, the gateway also checks that the method is allowed, that the tool call matches the tool's published schema, and runs a pattern check on the arguments for known prompt-injection phrases.

Then, safely. The call goes on to the tool's back end with a timeout. If that back end had been failing, a circuit breaker would stop sending it traffic for a while, rather than let every agent wait on it.

Finally, on record. The request is logged as metadata, with who called, which path, the result and how long it took, but not the body. Later that day an administrator tightens the policy rule, and that change goes into a separate audit log in which each entry carries the hash of the one before.

The gateway is built for the protocols agents use to reach tools, each other and people. MCP carries tool calls. A2A carries agent-to-agent calls, and the gateway publishes an Agent Card at the standard address, `/.well-known/agent-card.json` ². AG-UI and ACP stream an agent's events over server-sent events ³. HTTP/REST and GraphQL reach the systems an enterprise already runs.

It is not a model router. Choosing between language models is a different job, and a team that needs it runs it behind the gateway like any other back end.

Each control does one job. The gateway governs the agent's call, and Aavaran and Kavach are each designed to sit in front of the model as their own proxy. Aavaran is designed to sit in front of the model, as an OpenAI-compatible proxy or as a mask-before-inference call that a gateway or agent runtime makes, so that raw data does not reach a model unmasked. Kavach is designed to sit in front of the model as a proxy and screen each request for prompt injection and unsafe output; see Kavach.

Teams build agents in Flow Studio and Agent Lab and run them on the AgentAnywhere platform, and the platform's audit trail and observability record what they did.

The gateway runs in the team's own environment. It ships with a Docker Compose stack, with the gateway, Redis, Postgres, Prometheus and the admin console, and with a Helm chart for running the gateway on Kubernetes.

The admin console covers a dashboard, registered agents, monitoring and analytics, API keys, routes, a protocol tester, logs and settings. Logs can also be sent to Elasticsearch, Splunk, Amazon S3, Amazon CloudWatch or Datadog.

Copy it into your design review.

1. Every call carries an identity the gateway checks: a key that can expire and be switched off.

2. Limits are set per caller, per team and per use case, including token budgets that can stop calls.

3. What an agent may call is a written rule outside the agent: path, method, network, time.

4. Tool calls are checked against the tool's own schema before they are passed on.

5. Tool arguments get a pattern check for known prompt-injection phrases at the door.

6. Every call has a timeout, and a failing back end is taken out of the path until it recovers.

7. Each request is logged as metadata, and administrators' changes are kept in a log whose entries are hash-chained.

8. Masking personal data and screening model prompts are handled by controls built for those jobs.

9. The gateway runs in your own environment.

10. You can see it working: metrics, logs and a console your operators use.

The Agent Universal Gateway is not a model router, and it does not mask personal data or screen model prompts: those are Aavaran's and Kavach's jobs, each designed to sit in front of the model. Its admin audit log is a hash chain kept in the deployment's own database; it is not a signed record, and it covers administrators' changes, not every call. This post gives no performance figures.