From Explainability to Actionability: a Tiered Adaptable Multi-Agent Framework with Agent Reasoning Tools for Collaborative Failure Recovery

Abstract Effective human-AI collaboration, especially in failure scenarios, requires systems that function as active partners rather than static tools. This research addresses this requirement by introducing a tiered multi-agent architecture grounded in Human-Centered eXplainable AI principles. The architecture consists of three novel design artifacts: tiered reasoning that adapts explanation depth to failure severity, a traceable memory bus for auditability, and flexible reasoning tools for enhanced adaptability. These designs enable agents to not only perform evidence-based diagnoses of performance gaps but devise recovery strategies and propose actionable improvement plans as well. We empirically evaluate this architecture on an aspect term extraction task using hybrid methods that combine performance comparisons against state-of-the-art baselines, human expert user studies, and multi-role user simulations. The results demonstrate that our architecture significantly enhances both task performance and failure recovery diagnosis and plans. We then validate the generalizability of our architecture with a second task of comparable complexity. This research contributes an adaptable and generalizable framework and foundational artifacts for trustworthy AI teammates.