Type-Safe Agent Output Validation

AI models generate code, JSON, SQL, and structured text. These outputs fail silently in production. A model returns JSON that parses but violates a schema constraint; a generated function compiles but has an off-by-one error; a SQL query runs but returns the wrong rows. Traditional unit tests catch some of this, but they require developers to write the tests.

The insight from the community this week is that strongly-typed languages give the model a verifier it cannot ignore. Rust code that compiles has passed a meaningful set of correctness checks. Lean code can be verified at low cost. This suggests a market for tooling that applies the same idea to less-typed outputs: a validation layer that wraps agent output in a schema checker and rejects anything that does not pass before it reaches the application.

The starting point is an open source library that takes a Zod or JSON Schema definition and wraps any model call to retry until the output validates, with configurable retry budgets. Charge for the managed version that logs failures, surfaces patterns, and suggests schema improvements based on where models fail most often.