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This paper proposes GROUNDING.md, a framework for embedding scientific validity constraints directly into agentic AI workflows to ensure code accuracy in specialized domains like proteomics. By leveraging the superior adherence of agents to explicit rules compared to human developers, this approach aims to democratize software generation while maintaining rigorous domain standards. The work represents a significant step toward integrating epistemic grounding as a core component of future autonomous development pipelines.
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Agentic AI-assisted coding offers a unique opportunity to instill epistemic grounding during software development

arXiv3 min read

arXiv:2604.21744v2 Announce Type: replace-cross Abstract: The capabilities of AI-assisted coding are progressing at breakneck speed. Chat-based vibe coding has evolved into fully fledged AI-assisted, agentic software development using agent scaffolds where the human developer creates a plan that agentic AIs implement. One current trend is utilizing documents beyond this plan document, such as project and method-scoped documents. Here we propose GROUNDING$.$md, a community-governed, field-scoped epistemic grounding document, using mass spectrometry-based proteomics as an example. We have drafted a file specific to proteomics: proteomics_GROUNDING$.$md (available at https://github.com/OmicsGrounding/proteomics-grounding) to demonstrate what a GROUNDING$.$md would look like. This explicit field-scoped grounding document encodes Hard Constraints (non-negotiable validity invariants empirically required for scientific correctness) and Convention Parameters (community-agreed defaults) that override all other contexts to enforce validity, regardless of what the user prompts. In practice, this will empower a non-domain expert to generate code, tools, and software that have best practices baked in at the ground level, providing confidence to the software developer but also to those reviewing or using the final product. Undoubtedly it is easier to have agentic AIs adhere to guidelines than humans, and this opportunity allows for organizations to develop epistemic grounding documents in such a way as to keep domain experts in the loop in a future of democratized generation of bespoke software solutions.

Abstract:The capabilities of AI-assisted coding are progressing at breakneck speed. Chat-based vibe coding has evolved into fully fledged AI-assisted, agentic software development using agent scaffolds where the human developer creates a plan that agentic AIs implement. One current trend is utilizing documents beyond this plan document, such as project and method-scoped documents. Here we propose this http URL, a community-governed, field-scoped epistemic grounding document, using mass spectrometry-based proteomics as an example. We have drafted a file specific to proteomics: this http URL (available at this https URL) to demonstrate what a this http URL would look like. This explicit field-scoped grounding document encodes Hard Constraints (non-negotiable validity invariants empirically required for scientific correctness) and Convention Parameters (community-agreed defaults) that override all other contexts to enforce validity, regardless of what the user prompts. In practice, this will empower a non-domain expert to generate code, tools, and software that have best practices baked in at the ground level, providing confidence to the software developer but also to those reviewing or using the final product. Undoubtedly it is easier to have agentic AIs adhere to guidelines than humans, and this opportunity allows for organizations to develop epistemic grounding documents in such a way as to keep domain experts in the loop in a future of democratized generation of bespoke software solutions.

Software Engineering (cs.SE); Artificial Intelligence (cs.AI); Biomolecules (q-bio.BM)

arXiv:2604.21744 [cs.SE]

(or arXiv:2604.21744v2 [cs.SE] for this version)

https://doi.org/10.48550/arXiv.2604.21744 Focus to learn more arXiv-issued DOI via DataCite

https://doi.org/10.1021/acs.jproteome.6c00398 Focus to learn more DOI(s) linking to related resources

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Full text under the publisher's open licence · 325 words

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