Most AI agents don't fail the way they do in demos. They fail later, and quieter: a task runs at 2 AM, fails silently, nobody gets alerted, and you discover it the next morning — a full day of work gone. We run MeshCtx on a small three-machine cluster. Today's health check comes straight from a production instance that has been running for a while: 15/15 modules online, 0 errors, on v3.121.7. Where that stability comes from Part of the answer is test data we're happy to show: 3,728 automated tests, all passing, across Windows, macOS and Linux LongMemEval EM of 64.6% (3-sample best-of-3, vs a 62.5% symmetric baseline) At a 16KB memory budget: +16.7 percentage points — the tighter the budget, the bigger the gain MIT licensed — you can rerun the whole suite yourself Stability means three things It doesn't break. 3,728 tests across three platforms means the traps you might step into have very likely been stepped on by someone before you. Test coverage isn't a cost line — it's respect for the user's time. It remembers. Most agent failures are forgetting failures. Our answer is 17-region layered memory: a positions list doesn't bleed into an article draft, yesterday's task state doesn't overwrite today's. Remembering is table stakes; remembering the right things is the hard part. It behaves the same everywhere. Windows at the office, macOS at home, Linux in the cloud — the same tasks, the same behavior. Automation is a relay, not a restart. A cheap heuristic for choosing AI tools Check whether the team publishes its test numbers. Teams that put their report card in public usually have something to back it up. MeshCtx is free and open source (MIT): meshctx.com — run the tests, hit the health endpoint, don't take anyone's word for it. Including ours.