APEX: Adaptive Principle EXtraction A Three-Layer Self-Evolution Framework for Production AI Agents
Self-improvement in AI agents has emerged as a key research frontier: systems that modify their own prompts, workflows, and decision rules based on accumulated operational experience. The state-of-the-art Self-Harness framework [1] achieves 14--21% improvement on Terminal-Bench-2.0 by mining failure clusters and patching the agent harness. However, Self-Harness optimises only one dimension -- the prompt harness -- leaving behavioural principles and workflow topology unchanged. We propose APEX (Adaptive Principle EXtraction), a three-layer co-evolution framework that simultaneously evolves: (L1) the harness via failure-mode patching, (L2) behavioural principles via success-trace distillati...
| Authors | Ya-Chuan Chen, Tien-Jen Lai, Hsiang-Wei Hu |
|---|---|
| Primary category | cs.AI |
| Published | 13 June 2026 |
| Last updated at source | 13 June 2026 |
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