Agents4Gov: Privacy-Preserving Web Agents Using Open LLMs for Public Sector Tasks - Oliveira, João Victor de Castro; Rufino, Nathan; Levcovitz, Silvio; Utino, Matheus Yasuo Ribeiro; Lobato, Fábio; Turine, Marcelo; Rezende, Solange; Jacob Junior, Antonio Fernando Lavareda; Marcacini, Ricardo Marcondes (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC); 2025: Anais do XXII Encontro Nacional de Inteligência Artificial e Computacional, 2025)
Effective public governance requires an organizational culture that values transparency, technical competence, and ethical behavior, while also addressing persistent challenges related to efficiency and compliance in public sector institutions. The digital transformation of the public sector, particularly through the integration of Large Language Models (LLMs), represents a strategic shift toward data-driven governance, process automation, and scalable innovation. These emerging technologies offer unprecedented opportunities to enhance inclusiveness, transparency, security, efficiency, accessibility, and citizen-responsive public services. However, while closed-source LLMs can automate digital tasks, their adoption is limited by concerns over privacy and cost. We present Agents4Gov, a privacy-focused and cost-effective framework for automating browser tasks using open-source LLMs. Built on Browser-Use, it combines a powerful model for planning with a lightweight model for execution, supports asynchronous task requests via email, and can run on scheduled intervals to avoid the need for always-on infrastructure. Evaluated on MiniWoB++, Agents4Gov with LLaMA 4 models achieves competitive results compared to traditional Browser-Use agents powered by GPT-4o-based LLMs.