Most agents can respond to a prompt, but ask them to click a button in your enterprise software, and suddenly its limitations show. In the age of generative AI, everyone’s racing to build…
We are excited to announce a Salesforce AI Research and Berkeley collaboration: BFCL Audio—a new benchmark that extends BFCL to the audio domain! A little Berkeley lore: back in 2022, we couldn’t find…
The landscape of AI agent development has evolved rapidly, with developers needing robust frameworks to build, test, and benchmark intelligent systems. MCP-Universe emerges as a comprehensive solution, providing a modular framework designed around…
Let go of the fear of AI and see how digital labor can make your workday easier and more efficient.
Time series forecasting plays a central role in data-driven decision making. Yet, adapting forecasting models across different domains and temporal resolutions often requires custom engineering. This increases both development and maintenance costs —…
How “Move 37” points toward the future of synthetic business environments March 9, 2016. Seoul, South Korea. In the second game of the historic Go match between AlphaGo and grandmaster Lee Sedol, the…
How Engine and Agentforce Delivers Smarter, Faster Customer Support at Scale
Not all agents are the same, especially when it comes to enterprise tasks Building AI agents for CRM is much more than deploying a Large Language Model (LLM). An enterprise agentic system needs…
From Internet Wild West to Agent Interoperability Picture this: It’s 1981. In a university computer lab, a researcher sits before a glowing green terminal, attempting to access data from another institution. After hours…
The Challenge with Flows Today Salesforce flows sit at the heart of modern CRM automation, yet authoring them still requires a unique mix of declarative drag‑and‑drop and Apex know‑how. To ease this process,…
