Modern automation workflows increasingly rely on multiple AI platforms, each with distinct authentication methods, rate limits, and API schemas. Managing these differences at scale creates maintenance overhead and brittle systems. Unified connector interfaces address this challenge by abstracting platform-specific implementations behind consistent, reusable patterns. The core principle involves creating a translation layer that normalizes requests and responses across different services. Instead of writing custom code for each integration, teams define a standard interface that all connectors implement. This approach works particularly well when routing requests through services like MegaLLM, which can abstract multiple model providers behind a single API contract.…