Empirical analysis reveals persistent execution failures in multi-session AI-assisted research workflows, indicating that durable artifacts outperform behavioral rules.
Key Points
Identify why correctly formulated operational rules fail during research execution and determine engineering mechanisms that withstand human and automated inattention.
Classified twenty-eight failure instances from three multi-session AI-assisted research runs into five remedy-oriented categories.
Concurrently executed buffered write benchmarks (800 records per trial) on Windows to assess record loss under varying synchronization and append regimes.
Buffered appends under Windows concurrency lost records at an average rate of 2.4% across trials, exhibiting bimodal outcomes ranging between 0 and 96 missing records out of 800 per trial.
Per-record buffer flushing increased data loss rates roughly fifteenfold compared to standard buffered appends.
Only the combination of explicit locking, atomic append emulation, and disk synchronization (lock + O_APPEND + fsync) achieved zero record loss across all trial regimes in two independent implementations.