ML//agent//agent harness//harness equivalence

Harness equivalence is a proposed relation between two agent harnesses that holds when their internal differences stop mattering to the agent behavior that counts, so that one could stand in for the other. Two search commands can look identical to a caller while one uses literal matching and the other uses embeddings plus reranking.

Harness equivalence is a proposed relation between two agent harnesses that holds when their internal differences stop mattering to the agent behavior that counts, so that one could stand in for the other. Two search commands can look identical to a caller while one uses literal matching and the other uses embeddings plus reranking.

This is an open working question, not an established equivalence relation for end-to-end agents.

H1≈M,D,εH2H_1\approx_{M,\mathcal D,\varepsilon}H_2H1​≈M,D,ε​H2​

The statement is incomplete unless it names the model MMM, task distribution D\mathcal DD, metric, and tolerated difference ε\varepsilonε. Two harnesses may be equivalent in task success while radically different in latency, token cost, safety, or debuggability. Matching return types is not enough either: equivalent implementations must preserve the behavioral contract the model perceives, its hidden ABI.

Equivalence is model-relative. A terse schema that reliably guides one model may confuse another. It is also distribution-relative: search implementations that behave alike on application code may diverge on generated files or multilingual repositories.

Advice26 · sep 03Read noteBenchmark the whole agent interface; do not infer its behavior from implementation diagrams. Two harnesses that look identical on paper can steer the same model differently on the same tasks, and only measured behavior shows it.