DoggieHarness

LLM coding agents fail in a predictable, expensive way: they edit files they shouldn't, hallucinate changes that were never applied, break working code while fixing something else, and confidently report success on work that doesn't compile.

The stronger the model, the less often this happens, which has pushed the whole industry toward paying premium per-token rates for frontier models just to get reliable edits. The reliability gap, not raw intelligence, is the real cost driver.

DoggieHarness wraps a model in a discipline layer. First, self-audited file editing: every change the model proposes is validated against the actual file state before and after it is applied, so silent failures and hallucinated edits are caught at the point of edit rather than three steps later.

Second, work validation: the harness independently checks that completed work actually does what was claimed (it compiles, it passes, it matches the request) before the task is allowed to close. The model can't mark its own homework.

Because the harness catches and corrects the failure modes that separate weak models from strong ones, it lifts the floor: a smaller, cheaper, or local model inside DoggieHarness produces reliable edits that would normally require a frontier model. Frontier-grade reliability at a fraction of the token cost, or, run locally, at effectively zero marginal cost.

DoggieHarness is a bet against the "just use the biggest model" consensus: infrastructure that commoditizes the model underneath it. It also extends a family. DogOS disciplines robots; DoggieHarness disciplines coding agents. The thesis is the same: the intelligence is cheap, and the discipline layer around it is the moat.