A comprehensive AI management system requires more than just an LLM router. It needs three distinct gateways: a Model Gateway for controlling LLM access, an MCP Gateway for secure tool and data interaction, and an Agent Gateway to govern communication between different autonomous agents and provide a "kill switch."

Trying to secure AI agents by restricting which tools are exposed in the Model Context Protocol (MCP) is the wrong approach. Security should be implemented at the API layer itself using robust, granular permissions like OAuth scopes. Treat the AI agent as any other third-party application accessing your API.

Don't let LLMs make raw HTTP calls. Instead, provide a code execution tool with a statically typed SDK. This environment can run a type-checker, instantly catching errors when the model hallucinates a non-existent endpoint or parameter, then provide helpful, in-context documentation to correct its mistake.

Inference backends focus on complex runtime problems like GPU scheduling and quantization. API gateways should handle different concerns like request validation and lifecycle endpoints. Separating these layers prevents duplicating API logic across runtimes and allows each component to specialize, leading to a cleaner architecture.

A practical security model for AI agents suggests they should only have access to a combination of two of the following three capabilities: local files, internet access, and code execution. Granting all three at once creates significant, hard-to-manage vulnerabilities.

While starting with a vertically integrated system is fine, enterprises inevitably need two key components: an LLM Gateway to manage and route traffic to various models, and an MCP Gateway to securely connect those models to real-world systems.

Unlike model gateways managing simple API keys, tool (MCP) gateways handle greater complexity. They must interface with diverse authentication methods for different tools (e.g., Slack, Gmail) and manage granular read/write permissions to prevent autonomous agents from taking unintended actions with sensitive data.

For serious development or internal tools, logs are insufficient. An API gateway provides essential operational signals—like latency metrics, error rates by model, and readiness checks—that help diagnose failures unrelated to model quality. These gateway-specific metrics are crucial for building reliable systems on top of local LLMs.

Modern LLM clients expect more than just text generation. They require state management, lifecycle endpoints, and consistent API contracts, features often missing from local inference servers. An API gateway layer can bridge this gap between a simple model server and a full-featured platform.