The burgeoning field of LLM-based agents faces a critical bottleneck: efficiently re-executing complex tasks. Current self-evolution methods, relying on textual prompts or reflections, falter in intricate scenarios. A novel paradigm, AgentFactory, proposes a fundamental shift.
From Textual Memory to Executable Modules
AgentFactory redefines agent self-evolution by preserving successful task solutions as executable Python subagent code, rather than static textual experiences. This architectural change is crucial. These subagents are not merely saved; they are continuously refined based on execution feedback, leading to increasing robustness and efficiency over time. This approach directly tackles the limitations of prior methods by offering a tangible, reusable, and adaptable component for agent development.