Salesforce announced seven named “job-ready” Agentforce AI agents on September 11, 2026, each purpose-built for a specific business function. The announcement, timed ahead of Dreamforce 2026 (September 15-17 in San Francisco), also introduced the Trusted Enterprise AI Harness — a governance layer for companies already running multiple AI agent platforms.

This is not a concept demo. Salesforce reported that Agentforce and Slack have collectively delivered 7 billion Agentic Work Units, with 3.2 billion in Q2 alone. The agents being released now are the next step from generic AI assistants toward AI workers with names, defined roles, and measurable output.

Meet the Seven Agents

Hunter is the outbound sales agent. It works a sales pipeline from research to outreach, collaborating with sellers across weeks and months rather than handling one-off tasks. Currently in pilot with general availability planned for November 2026, Hunter has already produced results: one company reported that 60% of their sales pipeline is now built by Hunter.

Piper handles inbound pipeline generation — turning inbound interest into qualified opportunities without waiting for a human to follow up.

Casey is built for customer service, handling resolution workflows at scale across customer interactions.

Carter targets commerce, supporting order management, returns, and customer engagement in e-commerce environments.

Paige covers IT and HR — the internal workforce use cases where employees ask repetitive questions and need to navigate systems, policies, and workflows.

Marshall tackles supply chain operations, monitoring and acting on logistics, procurement, and fulfillment data.

Fin handles customer experience for enterprise clients, integrating capabilities from the Fin platform Salesforce completed acquiring on September 10, 2026. Fin brings an established global customer base of more than 30,000 companies into the Salesforce ecosystem.

Most agents are generally available now. Hunter remains in pilot until November 2026.

The Enterprise AI Harness: Governing Agents You Did Not Build

Alongside the agent portfolio, Salesforce introduced the Trusted Enterprise AI Harness — a six-part governance framework for enterprises that are already running agents from multiple vendors. The six pillars are: Trusted Context, Trusted Agency, Trusted Action, Trusted Governance, Trusted Security, and Trusted Models.

The Harness includes a new AI Control Plane for managing agents and AI systems across an entire organization — effectively a management layer that sits above any single platform. It is previewed ahead of Dreamforce but not yet fully available; broad customer availability is expected in early 2027.

This is a direct response to a real problem. Most large enterprises are not running one AI platform — they are running several simultaneously. Different departments have adopted different tools, and there is no unified view of what agents are doing, what they are accessing, or whether they are behaving consistently with company policy.

What This Means for Business

The shift from generic AI assistants to named agents with defined job functions matters for a few reasons.

Named agents make ROI easier to measure. When “Hunter” is responsible for outbound sales pipeline, you can track exactly what it generates versus what it costs. The 60% pipeline stat from the early deployment is the kind of number that moves a budget conversation from “AI strategy” to “agent headcount.”

Named agents also make organizational change easier. Telling a team “we are hiring Hunter to work outbound while the sales team focuses on closing” is a different conversation than “we are implementing an AI solution.” It maps to how teams actually think about work.

The Harness announcement signals where the enterprise AI market is heading: from buying agents to governing a workforce of them. The hardest part of scaling with AI agents is not deploying the first one — it is maintaining control when you have dozens running across the business.

For businesses evaluating where to start with AI agents, the message here is that the ecosystem is maturing fast. The question is no longer whether AI can handle these tasks. The question is whether your organization has the governance layer in place to run agents at scale — and what happens when agents from different vendors are all touching your data and your customers.

Enterprise DNA’s Omni platform takes the same approach: defining AI agents by role and function rather than by capability, with the governance and oversight built in from the start rather than retrofitted later.

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Ready to build your AI agent workforce? Enterprise DNA’s Omni team works with businesses to deploy agents that fit actual roles in your operations — not generic tools that need six months of configuration.

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