Rethinking Performance Debugging: From Optimization to Collaborative Reasoning

Abstract

Performance debugging in microservice systems is commonly framed as an optimization problem that seeks a single "best" root cause under accuracy-driven objectives. We argue that this framing creates an optimization trap: it obscures the reality of production environments, where evidence is incomplete, failures interact, and engineers must reason under uncertainty. This paper advocates a reasoning-centered perspective in which debugging systems generate, empirically challenge, and curate multiple hypotheses, treating explanations as first-class artifacts and accuracy as a constraint rather than the sole goal. We introduce the notion of a Reasoning Surface—a conceptual debugging artifact that maintains multiple, empirically grounded explanations and makes uncertainty and trade-offs explicit, rather than collapsing diagnosis into a single opaque result. Through an initial instantiation, we illustrate how this perspective leads to qualitatively different debugging behavior, particularly under operational stress, and argue for a shift beyond single-number leaderboards toward systems that support human-centered reasoning.