An AI agent has four key components
Posted: Sun May 25, 2025 4:52 am
Goal – the primary goal or task of the agent.
Environment – the contextual information, such as the goal, initial user input, history of previous activities or conversation, grounding data for relevancy, user feedback, and the data that the LLM has been trained on.
Reasoning – the inbuilt ability of LLM to make observations, plan next actions, and recalibrate to optimize toward the intended goal.
Action — typically external tools to enable an agent to achieve the goal. Some common examples of actions are information retrieval, search, code generation, code interpretation, and dialog generation.
How does Agentforce Assistant use LLMs as a reasoning engine?
Agentforce Assistant is Salesforce’s advanced AI-powered afghanistan phone number list conversational assistant, which interacts with a company’s employees and customers in natural language. Employees can use it to accomplish a variety of tasks in the flow of work, helping to increase productivity at scale. And consumers can use it to chat with brands and get questions answered immediately, leading to higher satisfaction and loyalty. Agentforce Assistant uses LLMs for language skills like comprehension and content generation and also as a reasoning engine to plan for complex tasks, thereby reducing the cognitive load on users.
Here’s how it works:
The user types the goal they want to accomplish, for example: “Build a webpage.”
Agentforce Assistant uses a curated prompt to send the user input to a secure LLM to infer the user’s intent.
Environment – the contextual information, such as the goal, initial user input, history of previous activities or conversation, grounding data for relevancy, user feedback, and the data that the LLM has been trained on.
Reasoning – the inbuilt ability of LLM to make observations, plan next actions, and recalibrate to optimize toward the intended goal.
Action — typically external tools to enable an agent to achieve the goal. Some common examples of actions are information retrieval, search, code generation, code interpretation, and dialog generation.
How does Agentforce Assistant use LLMs as a reasoning engine?
Agentforce Assistant is Salesforce’s advanced AI-powered afghanistan phone number list conversational assistant, which interacts with a company’s employees and customers in natural language. Employees can use it to accomplish a variety of tasks in the flow of work, helping to increase productivity at scale. And consumers can use it to chat with brands and get questions answered immediately, leading to higher satisfaction and loyalty. Agentforce Assistant uses LLMs for language skills like comprehension and content generation and also as a reasoning engine to plan for complex tasks, thereby reducing the cognitive load on users.
Here’s how it works:
The user types the goal they want to accomplish, for example: “Build a webpage.”
Agentforce Assistant uses a curated prompt to send the user input to a secure LLM to infer the user’s intent.