Evaluation integrity, memory efficiency, and observability become essential for fine-tuned models and agent systems

Harness-Zero, onPanda, Jev-Mem, CARE, Brackett, CodeMidas, and category-aware training point toward evaluation based on executable tasks, annotation quality, tool use, action completion, recovery, context adherence, latency, and monitoring. CodeMidas adds a promising code-environment RL direction, but its practical significance remains provisional until methods, artifacts, licensing, and independent results are available.