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Below the Harness: Governing a Multi-Model, Multi-Harness World
We believe the future is a multi-model, multi-harness world. And we think it needs a new trust model.
In 1988, Norm Hardy described a problem that had been quietly breaking systems for years: the confused deputy. A program that takes action using its permissions instead of yours.
Today, every AI agent is that deputy. It inherits your authority: Your credentials, your repo access, your ability to call APIs. But its behavior is probabilistic. It might be acting on an instruction found in its environment, on a step it invented, or on a confident wrong answer.
The industry didn’t fix the confused deputy problem by making the deputy itself more careful. They fixed it by moving its authority a layer away. Forty years on, that’s still the answer.
Everyone is converging on the same future
Three facts are pushing the industry toward the same conclusion.
Agents are expensive loops. An agent takes many steps, and you pay for every token of every one. We all can agree that it makes no economic sense to call the latest frontier model for simple tasks.
The leader of frontier capability changes often. We’re all aware that the top model of the day (and its vendor) changes every couple of months.
Your workflows may need custom models. Many teams are recognizing that intelligence is commodifying and the differentiator is custom models, derived from custom context.
As a result, all of us are quickly ending up with a portfolio of multiple models across multiple harnesses.
A similar convergence is happening one layer up. Developers pick certain tools for the right task, the way they always have. For example, perhaps Claude Code for long refactors, Codex for daily work, Hermes for quick scripts.
We believe the future is a multi-model, multi-harness world. And we think it needs a new trust model.
In 1988, Norm Hardy described a problem that had been quietly breaking systems for years: the confused deputy. A program that takes action using its permissions instead of yours.
Today, every AI agent is that deputy. It inherits your authority: Your credentials, your repo access, your ability to call APIs. But its behavior is probabilistic. It might be acting on an instruction found in its environment, on a step it invented, or on a confident wrong answer.
The industry didn’t fix the confused deputy problem by making the deputy itself more careful. They fixed it by moving its authority a layer away. Forty years on, that’s still the answer.
Everyone is converging on the same future
Three facts are pushing the industry toward the same conclusion.
Agents are expensive loops. An agent takes many steps, and you pay for every token of every one. We all can agree that it makes no economic sense to call the latest frontier model for simple tasks.
The leader of frontier capability changes often. We’re all aware that the top model of the day (and its vendor) changes every couple of months.
Your workflows may need custom models. Many teams are recognizing that intelligence is commodifying and the differentiator is custom models, derived from custom context.
As a result, all of us are quickly ending up with a portfolio of multiple models across multiple harnesses.
A similar convergence is happening one layer up. Developers pick certain tools for the right task, the way they always have. For example, perhaps Claude Code for long refactors, Codex for daily work, Hermes for quick scripts.