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MUSE-Autoskill is a framework for self-evolving agents — agents that don’t just run a fixed toolset but build, store, and refine their own skills as they work. Its structure is three coupled pieces: skill creation, memory management, and evaluation.

## Closing the self-improvement loop

Most agents are static after deployment: they fail the same way on the same task forever. MUSE-Autoskill targets the loop that fixes that. The agent creates reusable skills from its own experience, manages a memory that decides what to keep and what to discard, and runs an evaluation step that judges whether a new or revised skill actually helps. Each piece needs the others — skill creation without evaluation just accumulates noise, and memory without pruning eventually drowns the agent in stale routines.

## Why it matters

The hard part of lifelong learning agents isn’t generating new behaviours; it’s deciding which to trust and keeping the set coherent over time. Tying creation, memory, and evaluation into one framework is an attempt to make self-evolution stable rather than drift-prone. If agents are going to operate for weeks on long-horizon tasks, they need to get better from their own runs without a human shipping an update — and they need guardrails so “self-improvement” doesn’t quietly become self-degradation.