A developer has engineered an asynchronous recovery layer to resolve a critical failure mode in multi-agent large language model pipelines: silent schema corruption during fallback model swaps. While standard retry mechanisms successfully route requests to backup models when primary endpoints return rate-limit errors, they typically forward identical request payloads. This approach causes downstream agents to receive malformed or missing fields, rendering pipeline outputs structurally unusable while monitoring dashboards falsely report complete success. The proposed solution, shared via a public code repository, addresses this through four coordinated components. First, a precision error classifier replaces generic retry loops by categorizing failures into throttling, quota exhaustion, or context overflow events, each triggering a distinct operational response. Second, a payload normalization engine intercepts failed requests and reconstructs the API call specifically for the backup model capabilities, adapting system prompts and structural constraints to prevent format drift. Third, a state preserver captures the agent execution context, including message history and partial outputs, before the model switch occurs. Finally, an explicit resume message is injected into the backup model prompt, clarifying the pipeline stage and required output schema to prevent redundant execution or context loss. Controlled benchmarks evaluating a three-agent workflow, encompassing planning, execution, and validation, demonstrate the architecture effectiveness. In simulated throttling scenarios, baseline routing achieved full task completion but recorded zero percent schema integrity due to unaltered payload forwarding. The new recovery system maintained a hundred percent completion rate while preserving full structural compliance across all validation steps. The additional operational overhead, primarily a fifty-millisecond routing delay, remains negligible relative to typical inference latencies. The development underscores a broader industry gap in agentic infrastructure. Standard libraries treat model degradation as a network issue rather than a data contract violation. By decoupling routing logic from payload adaptation and enforcing explicit state tracking, the architecture ensures that fallback mechanisms function as true recovery pathways rather than silent data corruption vectors. The project rule-based configuration prioritizes auditability and cross-provider compatibility over opaque machine learning adapters, offering a transparent framework for production-grade agent resilience. The complete implementation requires no external dependencies and is designed for straightforward integration into existing async Python environments. The work establishes a new operational standard for large language model pipelines, demonstrating that successful task completion metrics must be paired with structural validation to accurately reflect system health.