AI Gateway

Here's the thing about AI agent traffic: it doesn't behave like human traffic. A single agent might make 50 tool calls in 10 seconds. It might chain together queries across multiple services. It doesn't have cookies or sessions in the traditional sense. Your existing API gateway wasn't built for this.

Without an AI Gateway, you're flying blind. You don't know which agents are calling your services, how much it's costing you, whether agents are accessing data they shouldn't, or if one rogue agent is about to hammer your database with a thousand queries.

An AI Gateway gives you the same visibility and control over agent traffic that you have over human traffic. Rate limiting per agent, cost attribution per tool call, audit logging for compliance, and real-time monitoring of what agents are actually doing with your services. For B2B companies exposing MCP servers or NLWeb endpoints, this isn't optional — it's how you keep agent interactions safe, reliable, and economically viable.

An AI Gateway intercepts all inbound agent traffic and applies several layers of processing:

1. Authentication. Verify the agent's identity. Is this Claude, GPT, or a custom agent? Does it have valid credentials? The gateway checks API keys, OAuth tokens, or MCP-specific auth before any request reaches your backend.

2. Authorization. Tool-level access control. Maybe Claude can call get_pricing but not update_account. The gateway enforces these permissions based on the agent's identity and your policies.

3. Rate limiting. Agent-specific throttling. Limit each agent to 100 tool calls per minute, or throttle specific high-cost tools. This prevents a single agent from consuming all your resources.

4. Request transformation. Clean up, validate, or enrich requests before they hit your backend. Strip invalid parameters, add default values, or inject context the backend needs.

5. Monitoring and logging. Every tool call gets logged with the agent identity, parameters, response time, and response size. This feeds dashboards that show you which agents are driving value and which are just burning compute.

6. Cost tracking. Attribute costs to specific agents, tool calls, or customer accounts. Essential when you're running MCP servers that cost you money per query.

A data analytics company exposes their platform through an MCP server with tools like run_query, get_dashboard, and export_report. They put an AI Gateway in front of it.

In the first week, the gateway catches that one customer's AI agent is running 3,000 queries per day — 10x their plan limit. Another agent is passing SQL fragments in the run_query parameters that look like injection attempts. A third agent from a recognized enterprise is consistently using get_dashboard with premium filter parameters their plan doesn't include.

The gateway blocks the injection attempts, throttles the overactive agent with a clear "rate limit exceeded" response, and logs the premium feature usage so the sales team can follow up with an upsell conversation. Without the gateway, all three issues would have hit the production database unfiltered.

• Using your existing API gateway unchanged. Traditional API gateways don't understand MCP tool calls, SSE streaming, or agent-specific patterns. You need gateway logic that's aware of the agent protocol layer.
• Rate limiting too aggressively. Agents that hit a rate limit and get a useless error will simply skip your service and recommend a competitor. Return helpful rate limit responses with retry-after headers and clear explanations.
• Not separating read and write operations. Read-only tool calls (get_pricing, search_docs) should have much higher limits than write operations (create_account, submit_order). Many teams apply uniform limits and either over-restrict reads or under-restrict writes.
• Ignoring cost attribution. If you can't tell which agent or customer is driving your MCP server costs, you can't price your service correctly. Build cost tracking into the gateway from day one.
• No graceful degradation. When your backend is down, the gateway should return a structured "temporarily unavailable" response — not let the connection hang until the agent times out.