video1.0<iframe src="https://www.loom.com/embed/b3635193ec6540c4adc305fab13e4a54" frameborder="0" width="1728" height="1296" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>12961728Loomhttps://www.loom.com12961728https://cdn.loom.com/sessions/thumbnails/b3635193ec6540c4adc305fab13e4a54-449586d91eb756cd.gif218.954This Loom explains how DejaVu detects and fixes production incidents by reusing prior lessons and safely managing memory. During a weekday lunch rush, all payment attempts failed, dropping orders from over 200 per minute to 9; DejaVu found a 41 millisecond match to ticket WM412 and attempted the prior fix, only to learn the issue was not in their code. It then used Nimble’s monitoring to identify LedgerLine’s breaking change, where a status field renamed from status to State, and DejaVu wrote a patch that made 12 out of 12 tests fail and deploy. The agent was tested for a simulated month with 39 pages, 35 false alarms, and 4 real incidents, and it stays sharp by deleting full conversation context after each page while preserving only auditable, safety-checked lessons in an append-only log.