Salesforce AI Research today unveiled new benchmarks, guardrails, and models aimed at enhancing the agentic AI in the enterprise. The goal, said Silvio Savarese, EVP and chief scientist of Salesforce Research, is achieving enterprise general intelligence (EGI), which he defined as business-optimized AI capable of delivering reliable performance across complex business scenarios while maintaining seamless integration with existing systems. “An agent is not just an LLM,” Savarese said in a roundtable discussion on Tuesday. “An agent is actually a complex system with four components: a memory, a brain, an actuator, and an interface.” As Savarese explained, memory enables agents to be persistent, facilitating their ability to retrieve useful information, such as best practices, policies, specific customer information, and previous conversations. The “brain” represents the agent’s ability to reason, plan actions, and orchestrate flows. The actuator, or function calls, allows the agent to execute actions planned by the brain. And the interface is how agents connect with humans through language, audio, video, and other modalities. “The brain and actuator go hand-in-hand,” Savarese said. “We are planning to power those using large action models (xLAMs). Large action models are specialized LLMs that have been explicitly trained to act and adjust their behavior to take into account that these actions are taken to environments.” Savarese stressed that Salesforce views autonomous agents as “force multipliers” for humans rather than replacements. |