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

2026-03-23

AI Summary

This work addresses the challenge enterprises face in deploying AI agents that balance capability, data sovereignty, and cost-effectiveness, a goal hindered by fragmented development pipelines in existing small models. To overcome this, we propose the first end-to-end platform that modularly integrates enterprise applications via the Model Context Protocol, establishing a closed-loop framework encompassing tool integration, programmatic environment encapsulation, trajectory synthesis, and continuous training and evaluation. This approach enables efficient generation of high-quality training data and supports automated model iteration. The resulting 8B-parameter model outperforms current small models by 10% on EnterpriseBench and CRMArena benchmarks, matching the performance of GPT-4o while reducing inference costs by 8–10×.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Multiagent Systems: Modeling other Agents
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

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's performance on complex enterprise workflows while reducing inference costs by 8-10x, and remain robust across diverse enterprise benchmarks, including EnterpriseBench (+10%) and CRMArena (+10%). EnterpriseLab provides enterprises a practical path to deploying capable, privacy-preserving agents without compromising operational capability.

Problem

• AI agents
• enterprise deployment
• data sovereignty
• development pipeline
• privacy-preserving

Innovation

• full-stack platform
• closed-loop agent development
• automated trajectory synthesis
• Model Context Protocol
• privacy-preserving AI agents