Agent builders rely on prompt-based self-reflection as a correction mechanism, but agents cannot detect errors that fall outside their own knowledge boundaries. Production systems need deterministic external validators (compilers, test suites, APIs, type checkers) as hard gates, yet no standardized integration pattern exists for wiring these into agent loops. This means self-correction is structurally unreliable, and errors that external tooling would catch trivially persist through to output.

Agent builders hand-roll brittle integrations with compilers, test suites, and type checkers for every agent loop; there's no standard way to wire deterministic validators into self-correction cycles, so trivially catchable errors ship to production.

Engineering teams building production AI agents (code-gen, data pipelines, workflow automation) who have already been burned by silent agent failures that a linter or test run would have caught.

Teams already pay for CI/CD, observability, and prompt-engineering platforms — this sits at the intersection of all three for agents; the pain is acute NOW because agents are moving from demos to production and every team is duct-taping their own validation wiring.

MVP: an open-source SDK (Python/TS) that exposes a universal `ValidationGate` interface with pre-built adapters for ~10 common validators (pytest, ESLint, mypy, SQL explain, JSON Schema, OpenAPI contract checks) plus a hosted registry where anyone can publish/discover gate plugins — think npm for agent validators.

Subset of the $5B+ AI developer tooling market; every team shipping agents in production (tens of thousands today, millions within 2 years) needs this layer.