Debugging concurrent Multi-Agent Systems (MAS) is inherently challenging due to non-determinism, asynchronous scheduling, and the tight interleaving of multi-threaded shared states. Traditional debugging methods often fail to capture the semantic intent of agent interactions or introduce significant performance overhead, known as the “Probe Effect.” This research proposes a generalized Hybrid Observability Framework for concurrent agent environments. The proposed methodology combines a low-overhead, asynchronous data ingestion layer with a neuro-symbolic analysis engine powered by Large Language Models (LLMs). Empirical results gathered via the FLASH-MAS platform demonstrate that an event-driven, lock-free buffering strategy allows for high-fidelity data capture with negligible impact on simulation timing. Furthermore, we formalize and evaluate an adaptive simulation protocol via a Reactive Hot-Swap by Cascade Propagation mechanism, designed to dynamically isolate non-deterministic cognitive entities while compressing global computational execution costs.