Agent assist systems are evolving from 'knowledge base search' to 'full-process intelligent companions.' According to a CCW Digital report, enterprises using large model-driven agent assist have seen average handle time reduced by 28%, and cross-selling success rates increased by 41%.
Core functions include: real-time conversation summarization—automatically generating problem summaries and historical correlations within 3 seconds of the customer speaking; real-time suggestions—recommending optimal scripts or offers based on customer sentiment and intent; and automatic compliance checks—ensuring agent language meets regulatory requirements for industries like finance and healthcare.
For example, an insurance company's agent assist system, upon detecting a customer mentioning that their auto insurance is about to expire, instantly displays the latest renewal plans, the customer’s past claims history, and predicts the discount range the customer is most likely to accept. The system also monitors the agent’s tone; if it detects customer dissatisfaction, it immediately suggests 'transfer to claims specialist' or 'issue a goodwill voucher.'
GlobalConnect's AgentAssist product has integrated the DeepSeek model, supporting offline mode (to ensure data privacy) and real-time knowledge base updates. One of its bank clients reported that agent training time was reduced from 4 weeks to 5 days, and the first-call resolution rate increased by 33%. Analysts believe that large model-driven agent assist will become the biggest productivity lever for contact centers in 2025.