ML//agent//agent harness//model-relative interface design

An agent interface is **model-relative** when its usefulness depends on the model and post-training policy consuming it. A schema can be valid for every MCP client while remaining cognitively ergonomic for one model, merely usable for another and actively confusing for a third.

An agent interface is model-relative when its usefulness depends on the model and post-training policy consuming it. A schema can be valid for every MCP client while remaining cognitively ergonomic for one model, merely usable for another and actively confusing for a third.

Names: Anthropic found that prefix and suffix namespacing have non-trivial effects that vary by LLM.

Formats: JSON, XML, Markdown and prose can produce different outcomes because models inherit different training distributions and parsing habits.

Granularity: Stronger models may exploit general shell tools; other models benefit from narrower operations that encode more workflow.

Budget: A coarse tool can dominate with five calls remaining while fine-grained tools preserve flexibility under a larger budget.

Versioning: Changing the model can invalidate interface optimizations even when every software type and endpoint remains compatible.

Portability: Syntax portability does not imply cognitive portability. Interoperability claims should include cross-model behavioral evaluations.

The practical consequence is co-adaptation: model and harness should be evaluated as a pair, and old scaffolding should be stress-tested whenever model capability changes.