arXiv:2607.02703v1 Announce Type: cross Abstract: 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
The rapid advancement of large language models is enabling more sophisticated agentic AI systems suitable for complex tasks like scientific software development.
This development indicates a significant step towards automating highly specialized and creative technical work, fundamentally altering how scientific research and software engineering are conducted.
Scientific software development could shift from manual coding to an AI-augmented or AI-driven process, leveraging structured knowledge and multi-cloud inference for greater efficiency and scalability.
- · Research Software Engineers
- · Cloud providers
- · AI platform developers
- · Scientific research institutions
- · Traditional software development consultancies
- · Manual coding bootcamps
- · Organizations slow to adopt AI tooling
Increased velocity and complexity in scientific software development.
Democratization of advanced scientific computing capabilities to a wider range of researchers.
The emergence of entirely new scientific fields and discoveries powered by AI-driven experimentation and software creation.
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Read at arXiv cs.AI