Enterprise AI Agents: From Prototypes to Production
TL;DR
This workshop aims to bring together researchers and practitioners to examine how enterprise AI agents can successfully move from prototypes to production, and focuses on three pillars: 1) Agent architectures and systems; 2) Enterprise applications and deployments; 3) Evaluation and governance.
Abstract
Large language models (LLMs) have evolved from standalone generative systems into agentic AI systems capable of planning, reasoning, tool use, and multi-agent collaboration. Enterprises are increasingly adopting AI agents to automate and orchestrate complex workflows, from IT operations to employee productivity. While early deployments focused on proof-of-concept prototypes, the past year has marked a clear shift toward production-grade enterprise AI agents. This transition has been enabled by a wave of new technologies, including multi-agent orchestration, memory and state management, skill-based and modular agent architectures, and deeper integration with enterprise data and workflow platforms, which together make scalable, reliable agent systems feasible in practice. At the same time, moving agents into production introduces new technical and organizational challenges, such as rigorous evaluation and benchmarking, security and governance, and system design for long-running, autonomous operation. Building on the success of our two prior highly attended editions: ''Agentic AI for Enterprise'' workshop at KDD 2025 and ''Enterprise RAG'' workshop at CIKM 2024, this workshop aims to bring together researchers and practitioners to examine how enterprise AI agents can successfully move from prototypes to production. We focus on three pillars: 1) Agent architectures and systems; 2) Enterprise applications and deployments; 3) Evaluation and governance.