Beyond Fault Localization: A Trajectory-Level Study of LLM Agents for Microservice Root Cause Analysis
Automated microservice root cause analysis (RCA) is usually evaluated by whether the final answer localizes the fault source. This is insufficient for on-call site reliability engineers (SREs), who need the evidence and reasoning behind the fault. Evaluating the process is hard because agent trajectories interleave free-form reasoning with heterogeneous tool calls and telemetry results, making it difficult to assess agents' behavior. To assess how well RCA agents ground diagnoses in evidence, we propose a trajectory-analysis methodology that characterizes evidence navigation and utilization, SRE-oriented diagnostic intent, and recurring failure modes. Our key insight is that fault-propagation paths give a causal reference for where agents investigate and how they use evidence. With it, we analyze 5,000 trajectories from ten framework-backbone configurations (nine agent frameworks, two backbone models) on RCABench. Correct localization can hide deficiencies in causal reconstruction. Successful investigations use retrieved evidence to guide later queries, consider broader diagnostic intents, and investigate more deeply. Failed investigations drift toward unrelated services or repeat shallow queries; recurring failures arise from omitted evidence, misinterpreted evidence, and unsupported reasoning. To test whether these patterns can guide improvement, we build a lightweight defense prototype targeting the three failure families. Across two held-out benchmarks, two backbone models, and six diagnostic frameworks, the failure patterns recur; the prototype reduces their prevalence and raises Acc@1 by 3.5-35.1 percentage points in every configuration, suggesting that the mined failures generalize and are actionable. Our findings motivate a process-level view of automated RCA, in which trajectories reveal whether a diagnosis is correct, well-grounded, and open to improvement.