Transferable Expertise for Autonomous Agents via Real-World Case-Based Learning

2026-04-14

AI Summary

This work addresses the challenge that large language model–driven autonomous agents struggle to effectively leverage task structure, constraints, and historical experience in complex tasks. To overcome this limitation, the paper proposes a case-based learning framework that extracts reusable knowledge assets—including task-specific knowledge, analytical prompts, and operational skills—from past experiences to enable cross-task and cross-agent knowledge transfer. Unlike conventional approaches that rely solely on pretrained knowledge or static prompting, this method supports structured task decomposition and informed decision-making. Evaluated on a unified benchmark encompassing six categories of complex tasks, the proposed approach consistently outperforms state-of-the-art baselines, with performance gains becoming more pronounced as task complexity increases, thereby demonstrating its effectiveness and scalability.

Technology Category

• Multiagent Systems: Multiagent Learning
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

LLM-based autonomous agents perform well on general reasoning tasks but still struggle to reliably use task structure, key constraints, and prior experience in complex real-world settings. We propose a case-based learning framework that converts experience from past tasks into reusable knowledge assets, allowing agents to transfer prior case experience to new tasks and perform more structured analysis. Unlike methods based mainly on pretrained knowledge or static prompts, our framework emphasizes extracting and reusing task-relevant knowledge, analytical prompts, and operational skills from real cases. We evaluate the method on a unified benchmark of six complex task categories and compare it with Zero-Shot, Few-Shot, Checklist Prompt, and Rule Memory baselines. Results show that our method achieves consistently strong performance across all tasks and matches or outperforms the best baseline in every case, with especially clear gains on more complex tasks. Further analysis shows that the advantage of case-based learning increases with task complexity, and that practical knowledge acquired by one agent can be reused by others. These findings suggest that case-based learning offers a promising path for building professional agents for real-world work.

Problem

• autonomous agents
• real-world tasks
• task structure
• prior experience
• complexity

Innovation

• case-based learning
• autonomous agents
• knowledge transfer
• real-world tasks
• structured reasoning