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This paper introduces Investigate-Consolidate-Exploit (ICE), a novel strategyfor enhancing the adaptability and flexibility of AI agents through inter-taskself-evolution. Unlike existing methods focused on intra-task learning, ICEpromotes the transfer of knowledge between tasks for genuine self-evolution,similar to human experience learning. The strategy dynamically investigatesplanning and execution trajectories, consolidates them into simplifiedworkflows and pipelines, and exploits them for improved task execution. Ourexperiments on the XAgent framework demonstrate ICE's effectiveness, reducingAPI calls by as much as 80% and significantly decreasing the demand for themodel's capability. Specifically, when combined with GPT-3.5, ICE's performancematches that of raw GPT-4 across various agent tasks. We argue that thisself-evolution approach represents a paradigm shift in agent design,contributing to a more robust AI community and ecosystem, and moving a stepcloser to full autonomy.

Investigate-Consolidate-Exploit: A General Strategy for Inter-Task Agent Self-Evolution