Core Viewpoint - Salesforce has shifted its strategy from relying heavily on generative AI to implementing more deterministic automation techniques in its Agentforce product, indicating a reconsideration of the effectiveness of large language models in business applications [2][4][15]. Group 1: Strategic Shift - Salesforce reduced its customer support team from 9,000 to approximately 5,000, citing cost savings through AI automation [2]. - The company has introduced basic "deterministic" automation in Agentforce to enhance reliability, moving away from the unpredictability associated with large language models [4]. - Salesforce's recent communications suggest that when Agentforce does not overly depend on large language models, its performance improves [3]. Group 2: Customer Feedback and Issues - Customers have reported various technical issues with Agentforce, including a phenomenon referred to as "hallucination," where the AI produces incorrect outputs [7]. - The cost of using Agentforce is high, with each interaction costing $2, leading to complaints about operational expenses [7]. - Vivint, a customer of Agentforce, experienced stability issues, prompting them to implement deterministic triggers to ensure consistent service delivery [8]. Group 3: Technical Limitations - Salesforce's CTO acknowledged that using basic automation can lower operational costs and improve reliability, but noted that exceeding eight instructions can lead to missed commands, which is critical for high-precision tasks [7]. - The company is testing a system called Agentforce Script to identify tasks that can be completed without relying on large language models, aiming to reduce unpredictability [9]. Group 4: Leadership and Future Directions - CEO Marc Benioff has indicated a shift in focus towards data infrastructure rather than AI models, highlighting the risks associated with unreliable AI outputs [13]. - There are discussions about potentially rebranding the company to "Agentforce," reflecting a strategic pivot in response to market interests [13]. - Salesforce's spokesperson emphasized the need for integrating AI with reliable data and business logic to achieve predictable outcomes, while also denying claims of reducing large language model applications [14].