Many organizations currently struggle to move AI agents from proof of concept to functional production environments. Internal teams often face significant hurdles when tuning retrieval-augmented generation (RAG), managing multi-agent orchestration, and ensuring data compliance. These technical barriers frequently lead to stalled projects and delayed business value.
MaiAgent Challenges Enterprise AI Development at VivaTech 2026
Building production-grade AI agents from the ground up is consuming months of valuable engineering resources for little return. At VivaTech 2026, Taiwan-based MaiAgent argued that enterprises should abandon custom-built systems in favor of a pre-governed AI Core to handle retrieval, orchestration, and security requirements.
MaiAgent aims to bridge this gap by providing an infrastructure layer that organizations can own and control. The platform features benchmark-validated retrieval accuracy exceeding 95% and utilizes the Model Context Protocol for native tool connectivity. According to CEO Scott Chang, the industry shift is moving away from the question of whether to adopt AI, toward how to make these systems reliable and governed at scale. Currently serving over 100 organizations across financial services, healthcare, and manufacturing, the company offers deployment flexibility through SaaS, private cloud, and on-premises options. As MaiAgent expands into European markets, the focus remains on providing a centralized hub for security, data sovereignty, and compliance. By abstracting the complex engineering of RAG systems, the company allows internal teams to prioritize integration and specific business outcomes over infrastructure maintenance.
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