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<p>Enterprise service systems increasingly require autonomous service composition and dynamic reconfiguration to automate complex workflows across organizational boundaries. LLM-based AI agents are being deployed to orchestrate specialized services for tasks ranging from supply chain coordination to business process optimization. A critical challenge in this autonomous service engineering is skill management: how can agents automatically discover, compose, and reconfigure service invocations in response to varying business contexts? Existing approaches treat skills as independent entities activated through semantic matching, failing to capture the sequential dependencies and context-dependent relationships governing effective service composition. We identify two critical challenges. First, skills exhibit long-range dependencies where one service invocation enables another several steps later, defying standard Markovian modeling. Second, skill relationships vary with operational context: optimal sequences differ between production validation and rapid prototyping. Drawing from computational neuroscience on hippocampal cognitive maps and episodic memory, we propose Skill Cognitive Maps for autonomous service planning. Our method learns a task-agnostic cognitive map capturing general skill dependencies through episodic trajectory embeddings, then adapts to specific contexts via context-gated planning. Our dual memory system enables episodic matrices to capture trajectory-level dependencies for generalization across service workflows, while task-specific patterns enable exact sequence retrieval for recurring business processes. Unlike methods requiring massive user data, our centralized approach constructs a single cognitive map that generalizes across tasks while adapting to context through efficient online updates, supporting continuous service lifecycle management. We provide theoretical convergence guarantees and analyze construction and retrieval complexity. Experiments on three datasets demonstrate superior performance over Context-Aware Q-Learning, LLM-Chain, ToolFormer, and Semantic-Tool, validating our dual memory architecture for autonomous service composition in enterprise value chains.</p>

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Skill cognitive maps: Learning task-agnostic skill relationships through episodic memory for context-aware planning

  • 期刊论文

  • 发表日期：

    2026年7月21日

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  • Zhengyu Wang,
  • Xianghui Hu,
  • Pan Li,
  • Kai Di,
  • Yichuan Jiang,
  • Tianyu Zuo,
  • Yuangan Wang,
  • Dong Shan,
  • Tianyi Liu

摘要

Enterprise service systems increasingly require autonomous service composition and dynamic reconfiguration to automate complex workflows across organizational boundaries. LLM-based AI agents are being deployed to orchestrate specialized services for tasks ranging from supply chain coordination to business process optimization. A critical challenge in this autonomous service engineering is skill management: how can agents automatically discover, compose, and reconfigure service invocations in response to varying business contexts? Existing approaches treat skills as independent entities activated through semantic matching, failing to capture the sequential dependencies and context-dependent relationships governing effective service composition. We identify two critical challenges. First, skills exhibit long-range dependencies where one service invocation enables another several steps later, defying standard Markovian modeling. Second, skill relationships vary with operational context: optimal sequences differ between production validation and rapid prototyping. Drawing from computational neuroscience on hippocampal cognitive maps and episodic memory, we propose Skill Cognitive Maps for autonomous service planning. Our method learns a task-agnostic cognitive map capturing general skill dependencies through episodic trajectory embeddings, then adapts to specific contexts via context-gated planning. Our dual memory system enables episodic matrices to capture trajectory-level dependencies for generalization across service workflows, while task-specific patterns enable exact sequence retrieval for recurring business processes. Unlike methods requiring massive user data, our centralized approach constructs a single cognitive map that generalizes across tasks while adapting to context through efficient online updates, supporting continuous service lifecycle management. We provide theoretical convergence guarantees and analyze construction and retrieval complexity. Experiments on three datasets demonstrate superior performance over Context-Aware Q-Learning, LLM-Chain, ToolFormer, and Semantic-Tool, validating our dual memory architecture for autonomous service composition in enterprise value chains.