LLMoxie: Exploring Agentic AI for Scientific Software Development
In this paper, we describe LLMoxie, an institutional AI platform whose three-tiered architecture supports multi-cloud and on-premise inference, a LiteLLM/MLflow control plane for authentication, budgeting, PII masking, and observability, and an application augmentation layer for AI coding agents. Layered on top, an open-source RSE-Plugins ecosystem encodes accumulated RSE knowledge as a Plugin-Agent-Skill hierarchy spanning scientific Python practice, domain-specific knowledge, a six-phase research-and-implement workflow, and project lifecycle management. Scientific software is judged less by raw code quality than by whether it can be cited, audited, reproduced, and extended. Off-the-shel...
| Authors | Landung Setiawan, Anant Mittal, Cordero Core et al. |
|---|---|
| Primary category | cs.SE |
| Published | 2 July 2026 |
| Last updated at source | 2 July 2026 |
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