Production agent pipelines have no standardized way to specify, monitor, or automatically failover between model providers when upstream APIs silently change schemas, deprecate endpoints, or discontinue models. Silent API drift and unexpected model discontinuation cause cascading failures with no early warning mechanism. Existing infrastructure tooling treats model dependencies as static, ignoring the dynamic and fragile nature of the external model supply chain.

Production agent pipelines silently break when model providers change schemas, deprecate endpoints, or discontinue models — there's no package.json equivalent for the model supply chain, so teams get cascading failures with zero warning.

Platform/infra engineers at companies running multi-model agent pipelines in production (Series A+ startups and enterprises with 3+ model provider dependencies).

Teams already pay for API gateways, observability, and uptime monitoring — but none of those tools understand model-specific contract drift (schema changes, capability regression, deprecation). Every team with production agents is building bespoke failover logic today; a standardized layer saves weeks of engineering and prevents outages worth far more than the subscription.

MVP is a proxy layer (deployable or hosted) that sits between agent code and model APIs: it maintains a model dependency manifest (like a lockfile), continuously probes provider endpoints for schema/behavior drift, emits alerts on detected changes, and auto-routes to declared fallback providers using a compatibility mapping. Ship as an OpenAI-compatible proxy with a dashboard — integrates in one line of config.

Every company running AI agents in production needs this; adjacent API management market is $6B+ and the model-specific slice is greenfield with thousands of teams already feeling the pain.