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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, Leiden2300 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:
- Computer Science
- Bioinformatics
- Artificial Intelligence
Background:
- AI-assisted coding is rapidly evolving, moving towards agentic software development where AI agents implement human-defined plans.
- Current trends involve using project- and method-scoped documents, but a need exists for broader, field-scoped guidance.
Purpose of the Study:
- To introduce GROUNDING.md, a community-governed, field-scoped epistemic grounding document.
- To encode Hard Constraints (scientific validity invariants) and Convention Parameters (community defaults) for AI-driven software development.
Main Methods:
- Proposing GROUNDING.md as a novel document structure.
- Utilizing mass spectrometry-based proteomics as a case study for implementation.
- Defining Hard Constraints as field-scoped validity rules and Convention Parameters as community defaults.
Main Results:
- GROUNDING.md enables the generation of scientifically sound code and tools by non-domain experts.
- Hard Constraints act as overriding validity constraints, ensuring empirical correctness.
- Convention Parameters provide community-agreed defaults, streamlining development.
Conclusions:
- AI agents can more reliably adhere to guidelines than humans, facilitating robust software development.
- GROUNDING.md keeps domain experts involved, enabling democratized generation of specialized software solutions.
- This approach enhances confidence for developers, reviewers, and users of AI-generated software.
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