Cloopen AI Tang Xingcai: Decoding New Quality Production Forces, The New Engine for Improving Efficiency in AI-Driven Enterprises

Recently, during the 2025 WAIC World Artificial Intelligence Conference, Cloopen AI successfully hosted the "Reinventing Lift Power - AI Initiates New Growth" AI AGENT Practical Implementation and Industry Innovation Forum. 

On the forum, Tang Xingcai, the product director of Cloopen AI's large model products, presented a speech titled "Decoding New Quality Productivity: AI - The New Engine for Enhancing Enterprise Efficiency and Effectiveness". He deeply expounded on how AI can reshape enterprise productivity and for the first time systematically demonstrated how Cloopen AI's intelligent agent (Agent) product matrix based on large models drives enterprises to achieve full-chain upgrades. 

The three elements of new quality productivity: Reconstructing the AI capability foundation of enterprises

• In terms of computing power, breakthroughs in hardware such as GPUs and NPU have brought about exponential growth in computing capabilities;
• In terms of data, the resources that enterprises can call upon have expanded from traditional structured data to deep knowledge data such as agent experience and service records;
• In terms of algorithms, large models have pushed AI to move from perception to the cognitive stage, and multi-modal understanding, deep reasoning, and generation capabilities create new value for business scenarios.

This transformation is driving enterprise customer service and marketing to fully evolve from traditional manual quality inspection, outbound calls, and other operational models to an "human-machine collaboration" intelligent new form, aiming to solve industry pain points such as inconsistent service standards, difficulties in experience inheritance, and low conversion efficiency. 

Four AI Agents: Driving Quality Improvement and Efficiency Enhancement Across the Entire Chain

Facing multiple challenges such as high costs in the customer service sector, difficulty in standardization, mechanical robot interaction, efficiency bottlenecks in the marketing sector, difficulty in experience transmission, and low conversion rates, Tang Xingcai proposed that AI should achieve an upgrade from single-point tools to an ecological capability through intelligent collaboration, helping enterprises achieve cost reduction, efficiency enhancement, and breakthroughs. 

Based on industry insights, Cloopen AI has launched an intelligent agent (Agent) system covering the entire process. Through intelligent collaboration, it achieves the transition from single-point applications to an ecosystem platform. He highlighted four core AI Agents: 

【Intelligent Quality Inspection Agent】
The traditional quality inspection methods rely on manual sampling or keyword rules, which have low coverage and high error rates, and are difficult to adapt to complex business scenarios. However, the Cloopen AI Quality Inspection Agent, relying on the semantic understanding and context-awareness capabilities of the large model, can achieve "high accuracy, high coverage, and high automation". 

In the financial marketing scenario, it is possible to precisely distinguish the language differences between "excessive commitment" and "reasonable guidance", avoiding rule errors. In document quality inspection, it is possible to automatically compare standard terms with agent language, quickly locating compliance risks. In self-inspection, combined with voice recognition technology, the identity of the customer can be verified. In addition, the built-in customer service polite questions, malicious complaints and other scenario templates can quickly adapt to enterprise needs, significantly improving the efficiency and accuracy of quality inspection. 

【Agent Assistant Copilot】
To address the issues faced by human agents, such as the need to memorize scripts and procedures, as well as the potential for emotional fluctuations or operational errors, Cloopen AI has developed Agent Assistant Copilot. This solution covers online consultation and voice conversation scenarios. It can call upon the knowledge base in real-time to recommend top-notch scripts and guide process nodes during communication, helping agents automatically fill out forms. 

Behind this is the support of RAG technology, ensuring that the recommended content is both accurate and up-to-date. This means that whether it is for novice customer service or cross-operation in multiple scenarios, it can achieve "expert-level" response quality. 

【Agent】
Unlike traditional voice robots based on keyword matching, the Agent of cloopen AI is a truly "understanding" intelligent entity driven by a large model. It can recognize spoken language, typos, and multi-round conversation context, and has stronger ability in intent recognition and emotion judgment. 

For instance, in bank customer service, it not only understands the true intention behind "I want to change to a credit card with a higher credit limit", but also guides customers to complete the operation in accordance with the compliance process, and automatically fills in the system process to ensure that intelligence and professionalism are given equal weight.
In addition, the agent has the ability to coordinate calls. When the robot makes outbound calls, human agents can monitor the conversation in real time, seamlessly take over when risks or opportunities are detected, and achieve "unnoticeable switching" through voice tone cloning technology, thereby improving both the efficiency and experience of outbound calls. 

【Insight Analysis Agent】
The enterprise conversation data contains a large number of unexplored business opportunities and risk signals. The Cloopen AI Insight Analysis Agent, through a large model, conducts full extraction and semantic decomposition of the conversation data, helping enterprises identify key information such as potential business opportunities, potential complaints, and conversion bottlenecks. 

In practical applications, this Agent can identify intents such as "loan" and "attention to the convenience of the process" from a single customer service conversation, and automatically label them as CRM leads or potential issues, generate action suggestions, recommend corresponding strategies. Furthermore, this capability has been deeply integrated with CRM systems, intelligent quality inspection, and robot platforms, enabling enterprises to build a set of "data-driven growth" intelligent operation systems. 

The intelligent agent platform serves as the foundation, and the open ecosystem accelerates collaborative innovation in the industry.

Tang Xingcai emphasized that the intelligent application of cloopen AI is not isolated but is built based on a unified intelligent agent platform. The platform achieves continuous accumulation of positive and negative examples through standardized data integration and the "data flywheel" self-optimization mechanism, driven by user feedback, to drive model self-optimization.

In addition, the platform supports protocols such as MCP and A2A, enabling mutual invocation among intelligent agents and ecological collaboration, providing enterprises with more flexible expansion capabilities.

Finally, Tang Xingcai concluded that AI is evolving from a "tool" to a "hub", becoming the core engine for enterprises to address challenges in efficiency, cost, and experience. cloopen AI will continue to deeply focus on the fields of customer service and marketing, through the construction of the intelligent agent ecosystem, to promote the deep implementation of AI new quality productive forces in enterprise scenarios, and help more enterprises achieve "improvement in quality and efficiency" and new growth.