That is a massive insight, and it hits on the exact reason the traditional "everything is an AI prompt" model starts to break down at scale.

When you build complex AI agent workflows, people often default to making a single large language model act as the router, the parser, and the executor. You end up with a brittle, monolithic chain of prompts where a tiny change in user input causes the model to misroute an entire task, wasting API tokens and spiking latency.

Intent Bus separates the cognitive routing from the infrastructure routing:

1. Deterministic Execution: Instead of relying on an LLM to consistently handle state, queue retries, or remember network addresses, the model's only job is to extract the core intent and parameters, then drop a structured payload into the bus.
2. Capability-Based Routing: You don't need a single worker device that has every python library, GPU, or automation SDK installed. By using worker capabilities (like required_capability="whisper" or gpu), the intent automatically finds the exact edge device or worker script equipped to handle it.
3. Decoupled Architecture: If an AI agent running in a cloud container decides a task requires a local desktop action or a mobile automation, it doesn't need an active, stateful socket connection to that device. It fires an intent to the bus and moves on. The edge worker claims it when it's free.

By treating agent actions as asynchronous, routed jobs rather than a continuous, synchronous chain of function calls, you turn a fragile AI pipeline into a highly stable, observable distributed system.