arXiv:2606.12834v1 Announce Type: new Abstract: As scientific workflows shift from deterministic executables to LLM-based agents, the development practices on offer, such as fine-tuning, reinforcement learning, and prompt-and-go, bury the scientist's judgment. We propose treating agent construction as a workflow stage and introduce AgentBuild, which builds a scientific agent from a contract the scientist authors. The contract is a version-controlled rubric, a difficulty-graded curriculum, and a curated external knowledge base. A rubric-driven judge gates a meta-optimizer coding agent that edit
The proliferation of LLM-based agents is leading to a demand for more systematic and controllable development practices, moving beyond ad-hoc prompting.
This paper proposes a framework for building scientific agents that emphasizes control and transparency, which is critical for trust and reproducibility in scientific research and industrial application.
The development of AI agents shifts from purely prompt-engineering or fine-tuning to a more structured, version-controlled 'contract-based' approach, integrating human judgment more formally into the build process.
- · AI developers
- · Scientific research institutions
- · Industries relying on automated scientific workflows
- · Software engineering practices
- · Ad-hoc LLM integration approaches
- · Organizations without structured AI development practices
Increased reliability and interpretability of scientific agents, accelerating research and development cycles.
The emergence of new standards and tools for agent development and auditing, creating a specialized engineering discipline.
Enhanced automation of discovery and design across various scientific fields, leading to unforeseen breakthroughs and industrial transformations.
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Read at arXiv cs.AI