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We're expanding our partnership with @Databricks to help organizations create a shared foundation for human and agent work.
As work increasingly happens through agents, automation, and conversational interfaces, having a complete, unified view of your business has never been more essential. By connecting governed enterprise data with customer and operational context, Salesforce and Databricks are empowering organizations to turn deep understanding into trusted, seamless action.
“The challenge is no longer building more agents. It’s giving agents the trusted data, business context, governance, and workflows required to operate safely at enterprise scale,” said Rahul Auradkar, President and GM, Data Foundations, Salesforce. “Together, Salesforce and Databricks are helping customers build a new operating model where humans and agents work side by side to accelerate productivity and business growth.”
To create this shared foundation, we are expanding our partnership across three key areas:
1. Governed Business Context: We are expanding our Zero Copy integration to extend trust controls across both platforms. With capabilities like Federated Authentication and identity mapping, organizations can ensure users and AI agents operate against the exact same governed enterprise context, permissions, and security controls—regardless of where the data resides.
2. Cross-Platform Discovery & Action: We are making it effortless to take action without custom integrations. Through Federated Search and bi-directional Model Context Protocol (MCP) integrations, users and agents can seamlessly discover information and work across Salesforce and Databricks.
3. Enterprise Context in the Flow of Work: We are bringing Databricks-powered insights and AI experiences directly into Slack. Whether it's asking Databricks Genie about inventory risks or getting real-time threat alerts through Databricks Lakewatch, teams and agents can access trusted information and accelerate decision-making without ever switching systems.
“Salesforce and Databricks help us bring together fan data from across our business into a trusted foundation for AI. With that context, our teams and agents can better understand each fan, recommend the next best action, and create more personalized experiences across every interaction.” — Joey Graziano, Chief Commercial Officer at Pacers Sports & Entertainment.
The gap between reasoning and action is officially closed.
Humans and agents, working side-by-side on a single, trusted foundation.
This is the new operating model for the enterprise.
@levie The "uniquely their own" is so key. Two companies can run the exact same model on the same task and get entirely different outcomes based purely on what data the agent can see and what it's authorized to do. That context is what creates the real differentiation.
You wouldn’t abandon an intern on day one, so stop leaving agents unmanaged!
@Asymbl_Inc coached and managed their Agentforce recruiter, resulting in 117 humans hired in 100 days.
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@gowithengine@NeuraFlash Handing off those routine chats means your experts have more time to build real customer relationships. That is a huge win for Engine. See you on Wednesday!
This is such an important point. Nobody has the playbook yet. What's becoming clearer is that spinning up an agent is getting easier, and the hard part is everything around it: data, governance, who owns the outcome when something goes sideways. That's where the real learning is accumulating right now.
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@alliekmiller The feedback step is crucial. It's easy to log what the agent did, but takes a bit more work to capture whether it was the right call against the original goal. Without that judgement loop closing, the memory layer just calcifies whatever the system did first.
@alexxubyte The inversion also rewrites the success metric. Pre-launch it's "did we ship?" Post-launch it's "is the agent getting smarter every week?" Loved this conversation with John.
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Evans is right that the volume goes up. The interesting question is where the new software gets built. A lot of what used to live in that fuzzy middle is going to get assembled on top of the systems that already hold the data and the rules, not next to them. Less greenfield app, more custom experience on a trusted base.