3 posts tagged with "JitAi"

While AI technologies, particularly large language models, have seen explosive growth in recent years, their enterprise-scale adoption has fallen far short of expectations. Countless AI projects remain stuck in the demo phase, failing to deliver meaningful business value. The gap between promise and reality remains vast.

Unlike traditional enterprise web applications, production-grade AI applications carry inherent complexity. They're fundamentally about executing tasks, not merely recording transactions. This requires deep integration with each organization's unique business processes, data ecosystems, and knowledge systems. Custom development becomes inevitable, yet the reality is prohibitively expensive and often disappointing. Traditional development paradigms are no longer sufficient—the market urgently needs AI-native engineering practices and development methodologies.

1. Enterprise Development Challenges in the AI Era​

Under the dual forces of AI advancement and its rapid evolution, enterprises are struggling with low development efficiency, high technical barriers, and poor system scalability. Traditional development takes months just to build basic infrastructure, while low-code platforms, though convenient at the start, quickly fall into the traps of limited flexibility and lack of AI-native support—making it hard to sustain complex business evolution.

An AI Agent (intelligent agent) possesses autonomous decision-making and task execution capabilities. It can automatically select appropriate tools based on user input and contextual information to complete complex business processes. An Agent consists of three core components: a system prompt, tools, and LLMS. Agents can also integrate with knowledge bases to implement Retrieval-Augmented Generation (RAG). This article will guide you through quickly creating a ReActAgent in Jitai, which combines reasoning and acting, and discuss the remarkably simple workflow and impressive results of AI Agents in Jitai.