Abstract

An agentic platform may capture operator satisfaction signals at scale without connecting that feedback to the specific skill artifacts that drove the evaluated responses. A system is proposed herein that closes this gap by attributing Ralph Loop feedback and agent execution trajectories to specific skill artifacts, aggregating signals across the operator population to localize underperformance to an implicated skill section, and dynamically tailoring diagnostic feedback questions as confidence in the problem diagnosis grows. The resulting closed-loop skill quality management system transforms failed or suboptimal agent executions into targeted skill improvements and validates each improvement by monitoring whether the Ralph Loop signal reaches an acceptable quality threshold before the updated skill is promoted to production.

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

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