ContextCraft: Root Cause Analysis with Tool-Augmented Agents

Root Cause Analysis (RCA) for modern distributed systems requires synthesizing gigabytes of heterogeneous observability signals during active incidents where engineers must restore service quickly despite noisy, delayed, or missing telemetry data. Recent LLM-guided RCA systems demonstrate that agents can iteratively query telemetry and propose diagnoses, yet they typically commit to a single conclusion via unstructured scratchpads with no mechanism to compare or audit competing hypotheses. We propose ContextCraft—a well designed agentic RCA system to operationalize Analysis of Competing Hypotheses (ACH) for AI agents augmented with a curated set of tools that are efficient in root cause localization. Across our evaluations, ContextCraft demonstrates that structured hypothesis–evidence reasoning can improve the accuracy–efficiency trade-off over free-form agentic RCA, achieving up to a 76.3% relative accuracy gain and 2.14$\times$ faster execution in our primary comparison, while producing fully auditable, evidence-grounded reports.