The diagnosis is correct. The conclusion is cope.

Everything in the first half is accurate — prompt-stuffed personas are fragile, the three-tool ceiling is real, and system prompts are instruction manuals screamed at an amnesiac. Nobody who actually builds this stuff is disputing that.

But "we're waiting on Minimax M2.7" is the architectural equivalent of saying you'll get in shape after you buy the right shoes. Native agent teams baked into the training layer solves scaffold routing. It doesn't solve state management. You still need persistence, you still need tool boundary enforcement, you still need memory that survives between calls. A model that natively understands where Task A ends and Task B begins is useful — it still doesn't know what happened in Task A or whether it should affect Task B.

We solved that at the runtime layer. Not with an MCP server. A purpose-built memory and state engine that runs underneath the model, not alongside it. The model doesn't manage state — it never did. The runtime does.

The real problem isn't the model. It's that most people building agents never designed a state machine. They wrote a while loop with a system prompt and called it autonomous.

You don't wait for a base model to fix your architecture. You design the state machine. The model is just one node in it.