Retrieval-augmented generation (RAG) is often proposed as a way to ground AI assistants in enterprise documents, but making it work for complex workflows can be fiddly. A new preprint from arXiv argues that a key part of the problem can be solved by introducing 'portable skills' — reusable modules that encode how to retrieve and apply information for specific tasks. The paper frames this as a case study in customer support, where agents must pull from SOPs and policy documents to handle requests.
The authors describe their approach as 'frustratingly easy,' suggesting that the main difficulty is not building a sophisticated agent but defining skills that can be moved across scenarios. The abstract emphasises the need to retrieve a complete and accurate set of relevant information from large operational corpora, which is the core challenge the method targets.
Since only this single source was provided, there are no conflicting findings to compare. The paper's full details, including experimental results, are not available in the abstract, so the claims here are limited to the authors' framing.