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

TL;DR

This paper introduces a case-based learning framework for autonomous agents that leverages real-world experience to improve task performance, especially on complex tasks, by transferring reusable knowledge assets.

Contribution

The paper presents a novel case-based learning approach that emphasizes extracting and reusing task-relevant knowledge from real cases, outperforming existing methods across diverse complex tasks.

Findings

The method achieves consistent strong performance across six complex tasks.

It outperforms Zero-Shot, Few-Shot, Checklist Prompt, and Rule Memory baselines.

The advantage increases with task complexity and knowledge transfer between agents is effective.

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.