A Flexible Multi-Agent LLM-Human Framework for Fast Human Validated Tool Building

• URL: http://arxiv.org/abs/2512.01434v1
• Date: Mon, 01 Dec 2025 09:19:18 GMT
• Title: A Flexible Multi-Agent LLM-Human Framework for Fast Human Validated Tool Building
• Authors: Daull Xavier, Patrice Bellot, Emmanuel Bruno, Vincent Martin, Elisabeth Murisasco,
• Abstract summary: We introduce CollabToolBuilder, a flexible multiagent LLM framework with expert-in-the-loop (HITL) guidance.<n>It iteratively learns to create tools for a target goal, aligning with human intent and process.<n>The architecture generates and validates tools via four specialized agents.
• Score: 0.8373057326694192
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce CollabToolBuilder, a flexible multiagent LLM framework with expert-in-the-loop (HITL) guidance that iteratively learns to create tools for a target goal, aligning with human intent and process, while minimizing time for task/domain adaptation effort and human feedback capture. The architecture generates and validates tools via four specialized agents (Coach, Coder, Critic, Capitalizer) using a reinforced dynamic prompt and systematic human feedback integration to reinforce each agent's role toward goals and constraints. This work is best viewed as a system-level integration and methodology combining multi-agent in-context learning, HITL controls, and reusable tool capitalization for complex iterative problems such as scientific document generation. We illustrate it with preliminary experiments (e.g., generating state-of-the-art research papers or patents given an abstract) and discuss its applicability to other iterative problem-solving.

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