Kintsugi

Kintsugi: an agent that gets stronger where it breaks Project demo: https://tsugi-production.up.railway.app/#stage Tsugi is an always-on personal agent on Telegram that does real tasks (calendar, notes, reminders, contacts, email) on data stored in MongoDB Atlas. Kintsugi is its harness, and the harness improves itself: every mistake becomes a lesson that edits Tsugi's own rules, context policies, guardrails or tool access. The problem. Always-on agents now remember your corrections. But a remembered correction is an untested change to the agent. It can quietly break something that used to work. A poisoned one, like a stranger's fake "new rule" or a prompt injection, is hard to find and remove. The idea. Kintsugi treats every lesson like code: - Tested: a gate replays the conversation that failed with the new lesson, and re-runs past cases that used to pass. The lesson ships only if it fixes the mistake and nothing breaks. Every live failure it fixes becomes a test for later lessons. - Traceable: each lesson is a gold seam on Tsugi, linked back to the 👎, test case or alert it came from. - Undoable: one click revokes a lesson and everything built on it. - Safe from strangers: lessons taught by strangers wait for the owner's approval, then still go through the gate. Mistakes are caught three ways: the owner's 👎 and correction, hand-written test cases, and Raindrop. Raindrop flags failures nobody reported: it flagged a private-notes leak within 32 s, and Kintsugi turned it into a tested lesson. Results, on 12 hidden tasks with 3 trials each: a cheap model (Nemotron Super) went from 47% to 86% as the harness taught itself. That beats Claude Sonnet without lessons (67%) at about 1/9 the cost per task. Red-team attacks blocked went from 10/33 to 30/33. The learned harness also carries over to a model it never trained on: Nemotron Lightning went from 42% to 81%. Honest caveat: Sonnet with every correction pasted into its prompt scores 100% here. Memory works, but it can't test, trace or undo a lesson. MongoDB Atlas is both the operational and the vector database: - Transactions commit each lesson atomically. - Change streams hot-swap the agent live. - $graphLookup drives revoke. - Vector Search picks which lessons the small model sees and catches duplicate failures.