Agentic AI systems powered by large language models are increasingly expected to solve capability-intensive tasks by using reusable external skills, which package instructions, tool-use procedures, executable code, and auxiliary resources. However, mainstream skill-based systems typically assume that available skills can be directly placed in the prompt. As skill libraries grow, this assumption becomes impractical: large corpora cannot be fully exposed to the model, while excessive candidate exposure can distract the agent and degrade skill selection. Therefore, scalable skill augmentation requires retrieving a small set of task-relevant skills from a large corpus before incorporating them into the agent’s context. We formulate this paradigm as Skill Retrieval Augmentation (SRA), where agents dynamically retrieve, incorporate, and apply reusable skills from large external corpora on demand. To systematically study SRA, we introduce SRA-Bench, which enables decomposed evaluation across three stages: skill retrieval, skill incorporation, and skill application. SRA-Bench contains 5,400 capability-intensive instances from six domains, 636 manually constructed gold skills, and a corpus of 26,262 skills. Experiments across multiple LLM and skill-use strategies show that SRA can consistently improve agent performance, but current agents struggle to realize this promise in practical pipelines. Specifically, they remain sensitive to distractor skills, lack need-aware skill loading, and often fail to translate relevant skills into correct task execution. Together, these results establish SRA as a distinct research problem and provide a foundation for developing scalable skill-augmented agents.

Continual post-training enables models to absorb emerging knowledge after deployment, but repeatedly updating shared parameters inevitably accumulates weight drift, which causes catastrophic forgetting and degrades general capabilities. Retrieval-augmented generation avoids such parameter drift, yet often lacks the depth of parametric knowledge integration. In this paper, we propose ReGrad (Retrievable Gradients), a new paradigm that treats gradients as retrievable units of knowledge. ReGrad pre-computes document-specific gradients offline, stores them in an indexed Gradient Bank, and retrieves only query-relevant gradients at inference time for temporary weight adaptation. However, raw language-modeling gradients are optimized for token-level document reconstruction rather than for query-driven knowledge use. We therefore introduce a bi-level meta-learning objective that reshapes document-derived gradients into generalizable adaptation signals for downstream tasks. Experiments across general and domain-specific settings show that ReGrad outperforms CPT and RAG baselines, enabling scalable and reversible parametric knowledge injection without accumulating weight drift.