Which use case is best supported by Salesforce Agent's capabilities?
A. Bring together a conversational interface for interacting with AI for all Salesforce users, such as developers and ecommerce retailers.
B. Enable Salesforce admin users to create and train custom large language models (LLMs) using CRM data.
C. Enable data scientists to train predictive AI models with historical CRM data using built-in machine learning capabilities
A. Bring together a conversational interface for interacting with AI for all Salesforce users, such as developers and ecommerce retailers.
Explanation:
Salesforce Agent (also known as Einstein Copilot or Agentforce) is designed to bring a natural language, conversational AI interface to Salesforce. It helps users interact with data and workflows across the platform using plain language — no coding or deep configuration needed.
The best-supported use case is:
🚀 Empowering all Salesforce users — including sales, service, dev, and commerce teams — to interact with AI through a conversational interface, grounded in real-time CRM and Data Cloud data.
🔍 Why Option A is correct:
"Conversational interface" is the core functionality of Agentforce.
Supports various personas like:
1. Sales reps (“Show me my pipeline”)
2. Service agents (“Summarize this case”)
3. Developers (integrating AI into flows/actions)
4. Commerce users (product recommendations, etc.)
Utilizes prompt templates, grounding, and Trust Layer to ensure accuracy and safety.
❌ Why the other options are incorrect:
B. Admins creating and training custom LLMs with CRM data
❌ This refers to Einstein Studio or Model Builder, not Agentforce.
Agentforce uses pre-trained LLMs (like OpenAI or Claude) with grounding — not fine-tuning.
C. Data scientists training predictive models on CRM data
❌ This describes Einstein Prediction Builder or Model Builder, focused on predictive analytics — not conversational AI.