Qianxi Yan, Chunrong Chen, Jiuzhou Zhao, Min Zhang, Yongzhou Xu, Xiaochuan Xu
6 min
Language agents are increasingly deployed for multi-turn interactions, knowledge retrieval, and tool invocation in complex domains like cloud services. However, maintaining the portable modules that encapsulate domain knowledge and handling procedures—known as Agent Skills—remains a major bottleneck. Traditionally, skills are either authored manually or optimized using single-turn question-answering evaluation.
The authors identify a fundamental limitation in existing self-evolution frameworks: once a single-turn evaluation patches obvious gaps, the evolution gradient decays, leaving multi-turn defects invisible. Furthermore, existing systems rely on scalar scores as a passive gate that can reject degraded candidates but cannot diagnose or repair structural causes. To solve this, the paper asks how to establish a continuous, closed-loop evolution process driven by trustworthy feedback and disciplined structural governance.
SkillEvo introduces a structured framework operating across two primary pillars: trustworthy feedback generation and controllable skill governance.
Trustworthy Feedback Generation: The framework converts multi-turn user simulation from a static evaluation endpoint into an active feedback generator. An intent state machine ensures complete coverage of user goals, dual-sided orthogonal evaluation separates simulator distortion from agent errors, and collective attribution filters out non-repairable failures. Only verified knowledge gaps are converted into evolution gradients.
Controllable Skill Governance: To prevent degradation across successive editing rounds, SkillEvo enforces fact consistency against dual anchors (comparing against both the production baseline and the previous round) and addresses structural integrity issues such as knowledge bloat, reference breakage, and factual over-generalization through diagnosis-driven active repair.
Evaluated across six categories of cloud services, 9 production Skills, and 98 skill-reference files, SkillEvo demonstrates substantial improvements over existing baselines. Task success rate (TSR) measurements show that SkillEvo outperforms the original baseline skills by 51.8 points, self-reflection-based evolution by 23.0 points, and single-turn-QA-driven evolution by 15.4 points. These results indicate that replacing single-turn QA with multi-turn trajectory feedback combined with active structural governance successfully prevents gradient decay and enables sustained skill improvement.
Automating the maintenance of agent skills is essential for scaling autonomous AI systems in enterprise environments. By demonstrating that feedback modality and structural governance are the binding constraints on skill evolution—rather than mere editing capacity or iteration count—SkillEvo provides a blueprint for building self-improving agents that adapt reliably to real-world user interactions without manual intervention.
Agent Skills are today either hand-authored or produced in a single LLM generation pass, and consequently possess no closed loop through which they might improve from the interaction failures they actually cause. Recent work does close this loop, but derives its feedback from single-turn question-answering evaluation. The consequence is a sharp asymmetry: once the first round has patched the gaps that a single exchange can reveal, the evolution gradient decays, the defects that surface only across multiple turns remain invisible, and evolution stalls. Governance in these systems is likewise driven by an end-to-end verification score, a scalar gate that can reject a degraded candidate but can neither localize nor repair its structural cause. We argue that the binding constraint on sustained skill evolution is neither editing capability nor the number of iterations, but whether the evaluation feedback keeps supplying trustworthy evolution gradients. We introduce SkillEvo, in which trustworthy feedback generates the gradient and controllable governance constrains its direction. The first component recasts multi-turn user simulation from an evaluation endpoint into a feedback generator: follow-up questions expose defects layer by layer, so that every round of revision both consumes feedback and produces new feedback. The second replaces the passive rejection of a scalar gate with an independent governance layer that actively repairs factual degradation and structural bloat, preventing the gradient from drifting as degradation accumulates. Across six categories of cloud services, 9 production Skills, and 98 skill-reference files, SkillEvo surpasses self-reflection-based evolution by 23.0 points and single- turn-QA-driven evolution by 15.4 points.
Sam: That's exactly the right concern, and the authors address it directly. The simulator is built from real human-handled support tickets. It extracts the customer's underlying goals, the sequence of topics they need to cover, and even their emotional tone across the conversation. An internal tracking mechanism then checks that every key issue the customer came in with has actually been raised and addressed before the conversation is allowed to end.
Alex: So it's not just generating random chatter—it's reconstructing how a real frustrated customer would actually behave.
Sam: Precisely. And on top of that, they add a screening layer that separates simulation noise from genuine agent failure. Some conversations might go wrong because the simulated customer behaved unrealistically, not because the agent's rulebook was flawed. That filter makes sure only trustworthy failure signals get used to update the knowledge base.
Alex: So bad data doesn't accidentally get baked into the rules.
Sam: Exactly. The second major pillar of the framework handles a different kind of problem: as the rulebook gets updated repeatedly, it can develop its own issues—outdated facts that contradict newer ones, or sections that have grown bloated and redundant. So there's an independent governance layer that actively monitors the knowledge base and repairs those structural problems as they accumulate.
Alex: So it's not just learning new things—it's also keeping the existing knowledge clean and consistent.
Sam: Right. Without that maintenance step, you could end up with a rulebook that's technically been updated many times but is internally contradictory and harder for the agent to use reliably.
Alex: When you put all of this together, how does it actually perform compared to older approaches?
Sam: The paper tests SkillEvo across 98 real-world cloud service reference files. Compared to a static rulebook that was written once by humans and never updated, SkillEvo improves task success by a substantial margin. And compared to systems that use a simpler self-reflection approach—where the agent just reviews its own past answers without the multi-turn simulation—SkillEvo still shows a meaningful improvement.
Alex: That's a notable gap. Does it run completely on its own once it's set up?
Sam: Not entirely, and the authors are upfront about that. The system still depends on having a supply of real human-handled customer tickets to generate its initial training signals. It can't learn from scratch with no prior data. And while it can propose edits to the rulebook and run its own internal quality checks, a human still needs to give final approval before any changes go live in a real product.
Alex: That's a reasonable safety guardrail, especially for something customer-facing.
Sam: It is. And it points to where the research sits right now—this is a meaningful step toward agents that can maintain and improve their own specialized knowledge over time, but it's not fully autonomous yet. The human is still in the loop at the critical decision point.
Alex: So the broader implication is that rather than retraining an AI from scratch every time its knowledge goes stale, you might eventually have systems that continuously patch themselves through realistic simulated practice—with humans reviewing the final output.
Sam: That's the direction the paper points toward. It's a more sustainable model than the current approach of periodic, expensive human rewrites. Whether it scales cleanly to more complex domains is still an open question, but the results here suggest the core mechanism is sound.
Alex: Thanks for walking through that, Sam. And thanks to everyone listening—this has been ResearchPod.