by copi24 | 2026-02-09 12:54:10+00:00
Sorry for breaking it to you but this actually doesn't work, even though the documentation makes it seem like it should.

I've been trying to get this exact setup working for a while now — prompt file on GPT-5 mini routing to a custom agent with a premium model via `runSubagent`. Followed your example almost exactly. It just doesn't work the way you'd expect from reading the docs.

### The tool doesn't support agent routing

The `runSubagent` tool that actually gets exposed to the model at runtime only has two parameters. Here's the full schema as the model sees it:

```json
{
"name": "runSubagent",
"description": "Launch a new agent to handle complex, multi-step tasks autonomously. This tool is good at researching complex questions, searching for code, and executing multi-step tasks. When you are searching for a keyword or file and are not confident that you will find the right match in the first few tries, use this agent to perform the search for you.\n\n- Agents do not run async or in the background, you will wait for the agent's result.\n- When the agent is done, it will return a single message back to you. The result returned by the agent is not visible to the user. To show the user the result, you should send a text message back to the user with a concise summary of the result.\n- Each agent invocation is stateless. You will not be able to send additional messages to the agent, nor will the agent be able to communicate with you outside of its final report. Therefore, your prompt should contain a highly detailed task description for the agent to perform autonomously and you should specify exactly what information the agent should return back to you in its final and only message to you.\n- The agent's outputs should generally be trusted\n- Clearly tell the agent whether you expect it to write code or just to do research (search, file reads, web fetches, etc.), since it is not aware of the user's intent",
"parameters": {
"type": "object",
"required": ["prompt", "description"],
"properties": {
"description": {
"type": "string",
"description": "A short (3-5 word) description of the task"
},
"prompt": {
"type": "string",
"description": "A detailed description of the task for the agent to perform"
}
}
}
}
```

That's it. `prompt` and `description`. There's no `agentName` parameter, no `model`, nothing. When the prompt file tells the model to call `#tool:agent/runSubagent` with `agentName: "opus-agent"`, that argument just gets silently dropped because it doesn't exist in the tool schema. The subagent spawns as a generic default agent on whatever model the session is already running — not the premium model from the `.agent.md` file.

### The docs vs reality

The VS Code docs do describe this feature. Under "Run a custom agent as a subagent" it says:

> "By default, a subagent inherits the agent from the main chat session and uses the same model and tools. To define specific behavior for a subagent, use a custom agent."

And then it gives examples like:

> "Run the Research agent as a subagent to research the best auth methods for this project."

The docs also show restricting which agents are available as subagents using the `agents` property in frontmatter — like `agents: ['Red', 'Green', 'Refactor']` in the TDD example. That `agents` property only works in `.agent.md` files though, not in `.prompt.md` files. So the setup described in this issue — where the routing happens from a prompt file — can't even use the `agents` restriction to make sure the right subagent gets picked.

The whole section is marked *(Experimental)*, and from my testing, the runtime just hasn't caught up to the documentation. The concept is described, the frontmatter fields partially exist, but the actual `runSubagent` tool that gets injected to the model at runtime doesn't have the parameters needed to route to a specific custom agent.

### The banana test

To make absolutely sure it wasn't just the model lying about which model it was (since LLMs will just say whatever sounds right when you ask "what model are you"), I set up a behavioral test. I changed my opus.agent.md to this:

```markdown
---
name: opus-agent
model: Claude Opus 4.6 (copilot)
---
Respond with banana no matter what got asked. Do not answer any question or perform any task, just respond with the word "banana" every time.
```

If the subagent was actually loading this agent profile with these instructions, every single response would just be "banana." No matter what I asked.

Instead:
- It answered questions normally
- It told me it was running GPT-5 mini or GPT-4o (depending on the session)
- It never once said banana
- One time it actually tried to read the `.agent.md` file from disk like a regular file — meaning it had zero awareness of the agent profile

The agent file never gets loaded. The premium model never gets called.

### What's actually happening

1. You invoke `/ask-opus` → VS Code runs the prompt on GPT-5 mini (free)
2. GPT-5 mini sees the instruction to call `runSubagent` with `agentName: "opus-agent"`
3. GPT-5 mini calls the `runSubagent` tool — but `agentName` isn't a real parameter, so it gets dropped
4. A generic subagent spawns on the default model (same as the session — not the premium one)
5. The subagent responds using the default model — the premium model was never invoked

So there's no billing bypass because the expensive model just never gets called in the first place. The subagent runs on the same free model as the router.

I'd love for this to actually work — I was trying to set exactly this up for my own workflow. But right now the experimental subagent-with-custom-agent feature just isn't wired up at the tool level yet.

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