Hindsight Remembered the Defect. SQL Checked the Evidence.
Hindsight Remembered the Defect. SQL Checked the Evidence. An AI agent remembering a previous manufacturing failure sounds useful. But what happens when it remembers the right symptom and associates it with the wrong root cause? That was the engineering problem I wanted to explore while building LotLens, a persistent-memory-powered investigation system for manufacturing defects. The idea behind…
The design of LotLens involved separating database records from agent memory. SQL stored the canonical manufacturing records while Hindsight retained derived investigation experiences. This distinction was crucial in determining which aspects of past incidents were supported by actual records.
To explore the potential for a system to remember the right symptom but associate it with the wrong root cause, three synthetic manufacturing cases were created. Despite sharing the same symptom of intermittent open-circuit readings during vibration testing, the underlying causes varied.
The first case, LOT-POC-001, was resolved through a corrective action confirming insufficient crimp height. The second case, LOT-POC-002, remained unresolved despite connector reseating and rework attempts. The third case, LOT-POC-003, was resolved by addressing terminal plating discontinuity causing contact surface corrosion.
When recalling past experiences using Hindsight, it was essential to validate the relevance and accuracy of the retrieved information. The application checked whether the recalled evidence matched records in the canonical database. Unresolved references were kept separately as unlinked memory references. This ensured that only validated historical records were included as canonical evidence in the investigation.
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