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

Researchers propose a case-based learning framework enabling LLM-based autonomous agents to extract and reuse knowledge from past tasks, improving performance on complex real-world problems. The method outperforms traditional zero-shot, few-shot, and prompt-based baselines across six task categories, with gains increasing as task complexity rises.

This research addresses a fundamental limitation in autonomous agent design: the difficulty of translating general reasoning capabilities into structured, reliable performance on complex real-world tasks. Current LLM-based agents often fail to effectively leverage task structure, constraints, and accumulated experience despite their strong performance on abstract reasoning problems. The proposed case-based learning framework bridges this gap by converting past task experiences into reusable knowledge assets, enabling agents to access both analytical prompts and operational skills developed in similar scenarios.

The work builds on growing recognition that pretrained knowledge alone insufficient for professional-grade autonomous systems. Previous approaches relied heavily on static prompts or rule-based memory, which lack flexibility and require extensive manual engineering. By emphasizing dynamic knowledge extraction from real cases, this framework represents a practical shift toward agents that improve through accumulated experience rather than architectural changes.

For the AI and enterprise automation sectors, these findings carry substantial implications. The research demonstrates that practical expertise developed by one agent becomes transferable to others, suggesting economies of scale in deploying multi-agent systems. Organizations deploying autonomous agents for complex workflows—legal analysis, financial modeling, technical troubleshooting—could achieve significantly higher reliability without constant prompt refinement or model retraining.

The scalability of case-based learning becomes especially valuable as task complexity increases, indicating the approach may solve a key bottleneck preventing autonomous agent adoption in professional environments. Future development should focus on standardizing case extraction across industries and creating frameworks for secure knowledge sharing between agents operating in different domains.

• →Case-based learning enables autonomous agents to transfer knowledge from past tasks, improving structured analysis on complex real-world problems.
• →The framework consistently outperforms zero-shot, few-shot, and rule-memory baselines across six task categories, with larger advantages on complex tasks.
• →Practical knowledge acquired by one agent transfers effectively to other agents, enabling scalable expertise sharing across multi-agent systems.
• →The approach addresses a critical gap where pretrained LLMs struggle with task constraints and structure despite strong general reasoning abilities.
• →Task complexity correlates directly with case-based learning advantages, suggesting the method solves bottlenecks in professional autonomous agent deployment.