LLM Fallbacks Break Agent Pipelines — I Built the Missing Recovery Layer

The article discusses how rate limits and incompatible payloads in large language models (LLMs) can disrupt workflows and corrupt data outputs. The author developed a recovery layer to handle these issues by classifying failures, adapting payloads, preserving execution states, and ensuring data integrity during model swaps. This innovation is crucial for maintaining seamless and reliable operations in AI-driven pipelines, especially as dependency on LLMs grows.