Science Consultant Agent

TL;DR

The paper addresses the challenge of choosing effective AI modeling strategies in a rapidly evolving landscape by introducing the Science Consultant Agent, a web-based pipeline consisting of a structured Questionnaire, automatic Smart Fill, literature-grounded Evidence-Based Recommendations via arXiv MCP, and a Prototype Builder that executes baselines on SageMaker. It details the end-to-end workflow, discusses design decisions and trade-offs (including context-building strategies and safe, tool-based prototyping), and presents initial user-focused evaluations. The work contributes an integrated framework that makes disciplined modeling decisions accessible to diverse users and outlines clear avenues for expanding sources, credibility filtering, and evaluation to improve robustness and impact. Practically, this approach promises faster, more reliable prototyping and better alignment between research guidance and implementable solutions in real-world AI projects.

Abstract

The Science Consultant Agent is a web-based Artificial Intelligence (AI) tool that helps practitioners select and implement the most effective modeling strategy for AI-based solutions. It operates through four core components: Questionnaire, Smart Fill, Research-Guided Recommendation, and Prototype Builder. By combining structured questionnaires, literature-backed solution recommendations, and prototype generation, the Science Consultant Agent accelerates development for everyone from Product Managers and Software Developers to Researchers. The full pipeline is illustrated in Figure 1.

Figures (3)

• Figure 1: The full pipeline of the Science Agent.
• Figure 2: Evidence Based Recommendation Generation.
• Figure 3: Prototype-Builder.