flowai-investigate

Root-cause analysis for complex software failures uses an iterative, hypothesis-driven workflow where you review a transparent board of hypotheses and probabilities, then select and approve diagnostic experiments to isolate the issue.

Hypothesis-driven debugging is an iterative process that tracks potential root causes on a transparent board, assigns probabilities, and requires explicit user approval before running controlled experiments to validate or reject each hypothesis.

Begin a guided investigation to generate an initial hypothesis board, then review the listed probabilities and evidence to select a specific hypothesis, design its corresponding experiment, and approve it for execution.

Yes, you can run controlled experiments for task-management bottlenecks while preserving system state, because the workflow tracks all diagnostic steps and restores the baseline between iterations to ensure auditability and safety.

No, the debugging workflow requires explicit user selection and approval before running any diagnostic experiments, ensuring you maintain full control over the investigation process and system modifications.

User-guided root-cause analysis relies entirely on user-initiated approvals and manual hypothesis selection, meaning it will not autonomously execute experiments or advance the investigation without continuous user interaction and decision-making.