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Salesforce has announced a new release of Agentforce that, it said, “gives teams the fastest path from AI prototypes to production-scale agents” — although with many of the new release’s features still to come, or yet to enter pilot phases or beta testing, some parts of that path will be much slower than others.
Agentforce 360 will be showcased at the company’s annual Dreamforce conference this week, and unites humans, agents, apps, and data, Salesforce president and chief engineering officer Srinivas Tallapragada said in a media briefing.
Customers are not currently getting value from their AI investments, he said. “[They] are stuck in what I call ‘pilot purgatory’,” he said. “And this is what we’re trying to solve.”
The Agentforce 360 platform provides a new human-readable portable JSON language, Agent Script, that lets teams author complex agent behavior. It enables hybrid reasoning through the Atlas Reasoning Engine, which is now configurable, and now supports Google’s Gemini large language model in addition to LLMs from OpenAI and Anthropic.
Agent Script will enter beta testing in November 2025.
Agentforce 360 will also include Agentforce Voice, a voice interface that Salesforce said will deliver “ultra-realistic” personalized voice experiences. It is connected to Salesforce Voice, and conversations are transcribed live, so human agents can both monitor them from the Service Console, and instantly take over the interaction should the agent be unable to fulfill a request.
Agentforce Voice is fully integrated into existing call center systems, with native connections to NiCE, Genesys, Amazon Connect, Five9, and Vonage. It will be generally available on October 21, although without support for SIP, WebRTC or Digital Channels, which will not enter pilot testing until December.
A new version of the agent development tool Agentforce Builder will enter beta testing in November. Users can describe the agent they want to build in natural language, and Builder then creates the agent using Agent Script. A preview panel showing every reasoning step and revealing agent behavior lets the developer test and debug it in the same application. A shared metadata model ensures the agents are portable and readable and enables their export via APIs, Salesforce said.
A new capability, Agentforce Vibes, lets developers use an agent in Agentforce Builder or their IDE of choice to automate complex development tasks throughout the software lifecycle and DevOps pipeline.
Finally, Context Indexing will be powered by Data 360 and will extract and structure data or surface unstructured data for use in Agentforce. Salesforce expects to make this feature generally available later this month.
Brian Gannuscio, SVP at Salesforce consultancy Coastal, said that the biggest problem would-be users of any AI are facing is data readiness.
There is a misconception that you can just flip a switch with AI, which has led to discomfort and unhappiness with the results, he said. “There are two things that really create a foundation to be able to do AI correctly. One is data readiness: You have to have access to the data in order for AI to be able to make smart decisions. Even in our own customer base, we find that our customers just don’t have a really strong data strategy. The second piece is prompt engineering, a new skill set that is rapidly evolving.”
AI agents need to be treated like interns on their first day on the job and provided with a lot of information, he said. For example, “if somebody is authenticating in through AI, you have to tell the agent that [users] can’t bypass the authentication and verification, that once they’re authenticated, they can’t switch their authenticated methods.”
