做自主智能体系统开发的团队终于有了能自动修复代码级故障的方案——MOSS 让智能体从用户交互中学习并自我改进，建议关注其源码级重写机制如何解决传统文本层无法触及的结构性问题。
现有自主智能体系统部署后基本静态，无法从用户交互中学习，重复故障需等待人工更新修复。MOSS 提出源码级自我进化方法，直接修改智能体系统的源代码，而非仅调整文本可变的技能文件或提示配置。系统通过自动收集生产故障证据、多阶段确定性流水线生成代码修改，并在临时工作器中验证候选版本，最后通过用户同意的容器热替换实现部署。在 OpenClaw 基准测试中，MOSS 单次循环将平均评分从 0.25 提升至 0.61，无需人工干预。该方法解决了文本层无法触及的结构性故障（如路由、钩子顺序、状态不变量等），为智能体系统持续自我改进提供了更通用的框架。
MOSS: Self-Evolution through Source-Level Rewriting in Autonomous Agent Systems
Autonomous agentic systems are largely static after deployment: they do not learn from user interactions, and recurring failures persist until the next human-driven update ships a fix. Self-evolving agents have emerged in response, but all confine evolution to text-mutable artifacts -- skill files, prompt configurations, memory schemas, workflow graphs -- and leave the agent harness untouched. Since routing, hook ordering, state invariants, and dispatch live in code rather than in any text artifact, an entire class of structural failure is physically unreachable from the text layer. We argue that source-level adaptation is a fundamentally more general medium: it is Turing-complete, a strict superset of every text-mutable scope, takes effect deterministically rather than through base-model compliance, and does not erode under long-context drift. We present MOSS, a system that performs self-rewriting at the source level on production agentic substrates. Each evolution is anchored to an automatically curated batch of production-failure evidence and proceeds through a deterministic multi-stage pipeline; code modification is delegated to a pluggable external coding-agent CLI while MOSS retains stage ordering and verdicts. Candidates are verified by replaying the batch against the candidate image in ephemeral trial workers, then promoted via user-consent-gated, in-place container swap with health-probe-gated rollback. On OpenClaw, MOSS lifts a four-task mean grader score from 0.25 to 0.61 in a single cycle without human intervention.