Your agent has the schema.It still doesn't know what it means.
Rusl turns JSON Schemas into living contracts: versioned, annotated, reviewable, and resolvable by humans, tools, and agents.
JSON Schema gives agents the shape, not the shared meaning. That shape is cheap to generate; the cost begins when every downstream system has to preserve the intent behind it. Rusl attaches the interpretations, examples, provenance, risk, migration guidance, and feedback agents need before they generate or modify code.
Same schema. Different assumptions.
Private data shapes create a tax on every integration. AI-assisted development feels fast because the first shape is cheap, then brittle because every downstream surface has to preserve the private assumption.
Compounding Token Cost is the downstream stack you now own.
The token cost to generate a schema is a rounding error. The expensive path is creating a local contract, then becoming responsible for every product and engineering surface that now has to support it: storage, HTTP exposure, UI, validation, mappings, tests, docs, and enough agent context to keep the next pass from drifting or reinventing it.
GitHub made code reuse normal. Rusl does the same for living data contracts.
Code became reusable when it had shared places to be published, discovered, reviewed, discussed, versioned, forked, and depended on. Data contracts need the same collaborative infrastructure.
Rusl applies that move to schemas and their meaning. The schema is the shape. The shared contract layer carries proposals, annotations, bundles, usage reports, source attestations, and trust signals that agents and humans can resolve together.
Drift happens at every boundary that touches data.
Storage validates shape. Domain code assigns meaning. APIs publish contracts. UI turns fields into interaction. MCP tools expose actions to agents. If those boundaries resolve intent privately, they drift separately.
Use contracts locally or resolve them live.
Both modes support the same goal: stop treating schema meaning as private memory inside one repo or one prompt.
Rusl makes the contract a shared dependency.
Version the shape, attach typed meaning, package related schemas, record feedback, and let agents resolve the same contract before they generate or modify code.
MCP tools expose schemas. Rusl answers what those schemas mean.
Tool parameters and results already have shape. The next failure is interpretation: how the schema should be used, which examples are trustworthy, what risk exists, and what an agent should do when meaning is ambiguous.