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

Short summary

The author presents a recovery layer for LLM-powered agent pipelines that handles rate limits and model swaps by classifying failures, adapting payloads across different model tiers, and preserving execution state and schema integrity throughout the switch. When fallback models receive payloads designed for the primary model, they often silently corrupt structured outputs—a subtle but critical failure mode. This solution maintains pipeline reliability during provider changes, addressing a major gap in production AI systems.

• •Fallback models often receive incompatible payloads, silently corrupting structured outputs
• •Author built a recovery layer that classifies failures, adapts payloads, and preserves state
• •Solves a critical gap in production LLM agent pipeline reliability

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