EvoAgent: An Evolvable Agent Framework with Skill Learning and Multi-Agent Delegation
Summary: arXiv:2604.20133v1 Announce Type: new
Abstract
This paper proposes EvoAgent – an evolvable large language model (LLM) agent framework that integrates structured skill learning with a hierarchical sub-agent delegation mechanism. EvoAgent models skills as multi-file structured capability units equipped with triggering mechanisms and evolutionary metadata, enabling continuous skill generation and optimization through a user-feedback-driven closed-loop process.
Key Features of EvoAgent
- Structured Skill Learning: EvoAgent organizes skills into multi-file structured capability units, allowing for enhanced skill management and development.
- Hierarchical Sub-Agent Delegation: The framework supports a delegation mechanism where sub-agents can be assigned tasks based on their specific skills, optimizing task execution.
- Continuous Skill Generation: Through user feedback, EvoAgent utilizes a closed-loop process to continuously enhance its skillset, ensuring that the framework evolves with user needs.
- Dynamic Task Decomposition: The three-stage skill matching strategy allows the framework to break down complex problems into manageable tasks, facilitating better problem-solving.
- Long-Term Capability Accumulation: By employing a three-layer memory architecture, EvoAgent can accumulate and retain capabilities over time, leading to sustained performance improvements.
Experimental Results
Experimental results based on real-world foreign trade scenarios demonstrate that, after integrating EvoAgent, GPT5.2 achieves significant improvements in professionalism, accuracy, and practical utility. Under a five-dimensional LLM-as-Judge evaluation protocol, the overall average score increases by approximately 28%.
Implications for Future Research
Further model transfer experiments indicate that the performance of an agent system depends not only on the intrinsic capabilities of the underlying model but also on the degree of synergy between the model and the agent architecture. This finding opens new avenues for research in the interplay between LLMs and agent frameworks, emphasizing the importance of collaborative design in developing more effective AI systems.
Conclusion
The introduction of EvoAgent marks a pivotal advancement in the field of AI, particularly in how agents can learn and adapt their skills over time. With its innovative framework, EvoAgent addresses several challenges in current AI systems, paving the way for more intelligent, responsive, and capable agents in various applications.