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What is Production-Grade AI Application?

· 5 min read

The industry is exploring how to deploy enterprise AI applications in production, but many attempts have taken the wrong path. Inability to make partial adjustments to outputs, deployment isolated from business systems, standalone UIs that can't collaborate with humans, and claims of being universal products—none of these represent what production-grade AI applications should be.

In traditional software development, AI coding tools like Cursor have significantly boosted developer productivity. These tools can generate numerous code files based on a developer's single instruction. However, what they generate is never the final correct result the developer wants. This is because large language models inevitably experience hallucinations—even when developers provide very clear information, AI-generated results may contain errors. Moreover, developers can't always articulate all requirements in a single attempt.

Unlike consumer-grade scenarios where users have high tolerance for AI output accuracy, production-grade content generation demands that AI applications provide continuous partial adjustment capabilities without affecting other correct parts. If every adjustment requires complete regeneration, users face long wait times and massive token consumption. How should production-grade AI applications support continuous partial adjustments to results?

In transactional record scenarios involving CRUD operations on data, traditional software modules offer lower costs and higher certainty. For business domains with fixed operational logic, traditional software modules remain appropriate. However, for transaction execution phases that require intelligence to determine how to proceed and execute operations, AI intelligence can be leveraged.

An AI system deployed in isolation from traditional business systems cannot fully understand existing software module capabilities, cannot conveniently invoke specific software modules, and cannot orchestrate execution sequences based on existing module capabilities. How should production-grade AI applications perceive, drive, and orchestrate traditional software modules?

Nearly all AI applications currently on the market feature dedicated standalone UIs for AI, typically chat interfaces. This model of interacting with AI through chat boxes represents most people's understanding of AI applications.

While useful, this approach falls short. AI's intellectual potential isn't fully realized. AI should function like a smart work partner, collaborating with humans on human-operable UIs. Humans should be able to confirm or adjust AI operations, and AI should execute business system operations on behalf of humans. How should production-grade AI applications support UI-based collaboration between AI and humans?

Business processes, rules, data, and models vary significantly across enterprises. Attempting to build a universal AI application is inherently impractical—production-grade AI applications must be custom-developed. How should production-grade AI applications support understanding and utilizing enterprise proprietary data, knowledge, and specific business models?

The industry continues exploring forms of production-grade AI applications without definitive conclusions. JitAi proposes a concrete definition: AI perception, driving, and orchestration of traditional software modules; UI-based collaboration between AI and humans; AI understanding and utilization of enterprise proprietary data, knowledge, and specific business models.

AI Perception, Driving, and Orchestration of Traditional Software Modules​

Traditional software systems are cost-effective and highly deterministic, remaining effective in domains with fixed operational logic. AI applications should natively integrate with them rather than replace them. AI must utilize enterprise proprietary information systems, embedding AI functionality into business workflows as various transaction execution assistants at different operational stages. These transaction execution AI assistants and the system's transaction recording management functions are natively integrated, mutually driving and invoking each other.

In JitAi's AI-native application architecture, applications follow the JAAP (JitAi AI Application Protocol). Each application module is self-describing, with descriptions generated during development that are both concise and accurate. AI can perceive system module capabilities without reading extensive code and documentation.

A business operation workflow typically requires invoking multiple independent system modules in a specific sequence. JitAi supports developing specialized AI assistants for specific business operations through a visual interface. System modules used by AI assistants are also added visually, including various business data CRUD functions, custom business functions, and frontend page operations. At runtime, AI drives invocations of specific functional modules based on business data and user requirements, intelligently orchestrating execution sequences across multiple modules.

UI-Based Collaboration Between AI and Humans​

As an intelligent assistant supporting human work, AI's decisions and actions can be observed and intervened upon by humans to jointly ensure work output correctness. In AI applications developed using JitAi, AI assistants not only converse with humans but can also operate all UIs that humans operate. AI operations are visualized in ways fully comprehensible to humans, and humans use the same UI to precisely intervene in AI results.

This AI capability to operate frontend UIs also benefits from JitAi's AI-native application architecture, where frontend functional modules, as components of system modules, can likewise be perceived, driven, and orchestrated by AI.

AI Understanding and Utilization of Enterprise Proprietary Data, Knowledge, and Specific Business Models​

No AI application can provide valuable enterprise services without knowledge of enterprise proprietary data, knowledge, and business models. This is why production-grade AI applications must be custom-developed—universal off-the-shelf products don't work.

Enterprise data and knowledge assets can be understood and utilized by AI through modeling. JitAi provides convenient domain structure modeling and model operation capabilities, enabling humans and AI to collaborate on UIs based on mutually understood models. Structure modeling includes data table model definitions, domain-specific Doc/Excel document template definitions, and other business entity object definitions. JitAi offers operational capabilities for various data models, which, as part of system modules, can be perceived, driven, and orchestrated by AI.

Conclusion​

Production-grade AI applications are not standalone chatbots, but rather multiple intelligent assistants deeply integrated with enterprise business systems. Enterprise intelligent transformation isn't simply adding AI features to traditional systems, but reconstructing business systems from an AI-native perspective to fully leverage AI intelligence.