[2603.21630] EnterpriseLab: A Full-Stack Platform for developing and deploying agents in Enterprises

Computer Science > Artificial Intelligence arXiv:2603.21630 (cs) [Submitted on 23 Mar 2026] Title:EnterpriseLab: A Full-Stack Platform for developing and deploying agents in Enterprises Authors:Ankush Agarwal, Harsh Vishwakarma, Suraj Nagaje, Chaitanya Devaguptapu View a PDF of the paper titled EnterpriseLab: A Full-Stack Platform for developing and deploying agents in Enterprises, by Ankush Agarwal and 3 other authors View PDF HTML (experimental) Abstract:Deploying AI agents in enterprise environments requires balancing capability with data sovereignty and cost constraints. While small language models offer privacy-preserving alternatives to frontier models, their specialization is hindered by fragmented development pipelines that separate tool integration, data generation, and training. We introduce EnterpriseLab, a full-stack platform that unifies these stages into a closed-loop framework. EnterpriseLab provides (1) a modular environment exposing enterprise applications via Model Context Protocol, enabling seamless integration of proprietary and open-source tools; (2) automated trajectory synthesis that programmatically generates training data from environment schemas; and (3) integrated training pipelines with continuous evaluation. We validate the platform through EnterpriseArena, an instantiation with 15 applications and 140+ tools across IT, HR, sales, and engineering domains. Our results demonstrate that 8B-parameter models trained within EnterpriseLab match GPT-4o...