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  "text": "New research from IBM Research on Self-Improving Agents.\n\nAgents have \"amnesia.\"\n\nAn agent that struggles with a particular API authentication flow today will struggle with the same flow tomorrow unless manually updated.\n\nThis paper introduces a framework for automatically extracting actionable learnings from agent execution trajectories and using them to improve future performance through contextual memory retrieval.\n\nThe system generates three types of guidance: strategy tips from successful patterns, recovery tips from failure handling, and optimization tips from inefficient but successful executions. A Trajectory Intelligence Extractor performs semantic analysis of agent reasoning patterns while a Decision Attribution Analyzer traces backwards through reasoning steps to identify root causes.\n\nOn the AppWorld benchmark, the memory-enhanced agent achieves 73.2% task goal completion compared to 69.6% baseline (+3.6 pp) and 64.3% scenario goal completion compared to 50.0% (+14.3 pp). The benefits scale with task complexity. Difficulty 3 tasks show the most dramatic improvements: +28.5 pp on scenario goals (19.1% to 47.6%), a 149% relative increase.\n\nWhy it matters:\n\nAgents that learn from their own execution traces, not just from training data, can systematically improve without manual prompt engineering.\n\nThe self-reinforcing cycle of better tips producing better trajectories producing better tips is a practical path toward self-improving agent systems.\n\nPaper: https://t.co/8IOIeEgFM5\n\nLearn to build effective AI agents in our academy: https://t.co/LRnpZN7L4c",
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