Your AI Agent Needs a Control Plane

A Reddit thread asked "Why don't we have a proper control plane for LLM usage yet?" and the question cuts to a real gap in how people build AI agents today. Most agents hardcode a single model, send every request to it regardless of complexity, and have no visibility into what they are spending.

The Routing Problem

Not every agent task requires the most capable model. Extracting text from a screenshot? A small local model handles that fine. Planning a complex multi-step workflow across five apps? That needs Claude or GPT-4 class reasoning. A control plane routes each request to the right model based on task complexity, latency requirements, and cost constraints.

For a desktop agent, this means simple actions like "what time is my next meeting" get answered by a local model running on Apple Silicon in milliseconds, while complex tasks like "reorganize my project files based on the structure described in this document" get routed to a cloud model with stronger reasoning.

Token Budgets and Cost Visibility

Without a control plane, agent costs are invisible until you get the bill. A token budget system sets limits per task, per day, or per user. When the agent approaches a budget limit, it can switch to cheaper models, batch requests more efficiently, or ask for approval before continuing.

This is especially important for agents that run autonomously. An always-on desktop agent processing background tasks can quietly burn through API credits if there is no budget enforcement.

Retry with Fallback

APIs fail. Rate limits hit. Models go down for maintenance. A control plane handles this gracefully by retrying with a fallback model instead of crashing. If Claude is unavailable, fall back to a local model for basic tasks and queue complex tasks for retry. The user should not notice unless the degradation is significant.

The audit logging piece ties it all together. Every LLM call gets logged with the model used, tokens consumed, latency, and cost. This turns agent infrastructure from a black box into something you can actually optimize.

Building a control plane is not exciting work, but it is the difference between a demo and a production agent.

Fazm is an open source macOS AI agent. Open source on GitHub.