AGENTIC-SYSTEMSMarch 20, 20261 min read9 views

Memento-Skills: Let Agents Design Agents

Memento-Skills is a generalist LLM agent system that functions as an 'agent-designing agent,' autonomously constructing and improving task-specific agents through a memory-based reinforcement learning framework.

Computer Science > Artificial Intelligence

Title: Memento-Skills: Let Agents Design Agents

We introduce Memento-Skills, a generalist, continually-learnable LLM agent system that functions as an agent-designing agent: it autonomously constructs, adapts, and improves task-specific agents through experience. The system is built on a memory-based reinforcement learning framework with stateful prompts, where reusable skills (stored as structured markdown files) serve as persistent, evolving memory. These skills encode both behaviour and context, enabling the agent to carry forward knowledge across interactions.

Starting from simple elementary skills (like Web search and terminal operations), the agent continually improves via the Read–Write Reflective Learning mechanism introduced in Memento 2. In the read phase, a behaviour-trainable skill router selects the most relevant skill conditioned on the current stateful prompt; in the write phase, the agent updates and expands its skill library based on new experience. This closed-loop design enables continual learning without updating LLM parameters, as all adaptation is realised through the evolution of externalised skills and prompts.

Unlike prior approaches that rely on human-designed agents, Memento-Skills enables a generalist agent to design agents end-to-end for new tasks. Through iterative skill generation and refinement, the system progressively improves its own capabilities. Experiments on the General AI Assistants benchmark and Humanity's Last Exam demonstrate sustained gains, achieving 26.2% and 116.2% relative improvements in overall accuracy, respectively.

Source: arXiv cs.AI Recent

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