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Agentic AI-Assisted Coding Offers a Unique Opportunity to Instill Epistemic Grounding during Software Development
Magnus Palmblad1, Jared M Ragland2, Benjamin A Neely2
1Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden 2300 RC, Netherlands.
Journal of Proteome Research
|July 17, 2026
Summary
AI-assisted coding is advancing rapidly. GROUNDING.md, a new document type, uses Hard Constraints and Convention Parameters to ensure scientific software correctness and democratize development.
Area of Science:
- Computational Biology
- Software Engineering
- Proteomics
Background:
- AI-assisted coding is rapidly evolving, moving towards agentic software development where AI agents implement developer-created plans.
- Current trends involve using project- and method-scoped documents, but a gap exists for broader scientific field guidance.
Purpose of the Study:
- To introduce GROUNDING.md, a novel community-governed, field-scoped epistemic grounding document.
- To demonstrate how such documents can encode scientific validity invariants and community defaults for AI-driven software development.
Main Methods:
- Developed the concept of GROUNDING.md as a field-scoped document.
- Defined "Hard Constraints" (empirical validity invariants) and "Convention Parameters" (community defaults).
- Used mass spectrometry-based proteomics as a case study for implementation.
Main Results:
- GROUNDING.md provides a framework for encoding non-negotiable scientific validity invariants and community-agreed defaults.
- This document type enables AI agents to adhere to field-specific best practices.
- Facilitates the generation of scientifically sound code and tools by non-domain experts.
Conclusions:
- GROUNDING.md empowers democratized, bespoke software generation by embedding best practices at the ground level.
- It enhances confidence for developers, reviewers, and users of AI-generated software.
- This approach keeps domain experts involved while leveraging AI for efficient software development.
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