In recent years, the rapid evolution of Large Language Models (LLMs) has turned "AI + Scientific Computing" into a highly active frontier. Whether in molecular dynamics, material and drug design, or quantum computing, numerous platforms are attempting to bridge natural language interfaces with rigorous scientific computation.
From a user experience perspective, this approach significantly lowers the barrier to entry, allowing non-experts to breeze through standardized experimental workflows. However, when we shift our focus from "Can it run a standard experiment quickly?" to "Does it support open-ended scientific exploration?", a stark architectural divide emerges regarding abstraction boundaries and system openness.
Currently, Scientific Computing Agent systems can be broadly categorized into two technical paradigms:
Encapsulated Systems: Running in controlled cloud sandboxes, these systems typically provide pre-configured, templated workflows accessible via a Web UI.
Open & Programmable Systems: Operating within general-purpose computing environments, these systems (like Claude Code or Codex) integrate deeply with code repositories, runtimes, and external toolchains.
