AI Models & Platforms

Salesforce Debuts Job-Ready Agentforce Agents and Long-Horizon Runtime

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Salesforce on September 11, 2026, introduced a portfolio of job-ready Agentforce AI agents built for work across sales, service, commerce, employee experience, and the back office, saying it has delivered 7 billion Agentic Work Units across Agentforce and Slack.

The announcement follows two years of work with customers across thousands of agentic deployments, and Salesforce said the 7 billion total includes 3.2 billion Agentic Work Units (AWUs) delivered in the second quarter alone. The company framed the launch as a shift from getting AI to answer questions toward getting AI to do work across business functions. Salesforce introduced the AWU in a February 25, 2026, article, defining one AWU as one discrete task accomplished by an AI agent and describing it as a platform-level metric spanning Agentforce and Slack AI. The new agents are connected to Customer 360, so they can work with the customer context and business processes companies already have in Salesforce.

Agents Built for Specific Jobs

Instead of building every agent from scratch, companies can start with agents designed around the jobs where Salesforce said AI can have an immediate business impact. Each agent ships with the skills, actions, and data models required for its job and can be tailored to how each company works, including giving the agent its own name.

Casey, a help agent that resolves customer service issues across voice, SMS, WhatsApp, and web chat with pre-built support for FAQs, returns, account management, and human escalation, is generally available, as are Paige, an IT and HR employee agent that resolves requests across Slack, portals, and the tools employees already use, and Carter, a shopper agent that helps shoppers discover and compare products, get questions answered, and convert with in-chat checkout.

Also generally available are Marshall, a supply chain agent that orchestrates end-to-end back-office processes, automates manual work with deterministic execution, and provides an audit record of every action; Piper, an inbound pipeline-generation agent that works across websites and inboxes to engage, qualify, and convert inbound leads for B2B sales and marketing teams; and Fin, a customer agent that resolves complex customer-experience workflows across every channel. Salesforce said Fin is powered by Operator, a customer operations agent, and Fin Apex, a set of custom models trained for customer experience.

Hunter, an outbound sales agent that works a sales pipeline from research to outreach and collaborates with sellers over weeks and months, is in pilot, with general availability planned for November 2026.

Salesforce said every agent operates within the customer’s business rules, permissions, and security. With Agent Script, the company’s open-source language for agent behavior, customers can combine AI reasoning with deterministic rules for what Salesforce described as granular control over how agents make decisions and take action.

Customer Results Cited by Salesforce

Salesforce reported early results from named customers already running the agents. It said 50% of Engine’s chat inquiries are fully resolved by its help agent, Eva, and that 60% of Perk’s sales pipeline is built by its outbound sales agent, Hunter. According to the company, 70% of Autism Queensland’s administrative requests are resolved by its employee service agent, Paige, and Hibbett AI handles 90% of core shopper journeys after going live in six weeks.

Asana’s website agent, Piper, drives 4x the conversation volume, and customers deploy Piper in 45 days on average, Salesforce reported. It also said 79% of Anthropic’s conversations that Fin sees are resolved autonomously.

Long-Horizon Runtime for Multi-Day Work

Salesforce also built a long-horizon runtime for Agentforce that it said enables agents to pursue goals across days and weeks instead of completing only a single task or interaction. In the company’s example, a seller can ask Hunter to rescue at-risk deals before the end of the quarter. Hunter turns that objective into a measurable goal, builds a plan, and determines the tasks, tools, and context required, applying guardrails that define when it can act autonomously and when seller approval is required.

Three capabilities underpin the runtime, according to Salesforce. Memory preserves context and progress across sessions so work does not stop when an interaction ends. Durable execution keeps plans running over time and lets an agent resume or course-correct as circumstances change. Dynamic steering adapts an agent’s behavior based on an individual user’s feedback and direction.

Salesforce said Hunter is the first agent to run on the long-horizon runtime, that more agents across the portfolio will run on it over time, and that customers will be able to build long-horizon agents of their own with Agentforce.

Platform Additions and Rollout Dates

Agentforce Coworker gives employees an agent that works alongside them across the surfaces where they work, grounded in the context of their business, according to Salesforce. With AI Skills in Agentforce Coworker, employees can teach Coworker how to complete a task once and then scale that know-how across the workforce and across interfaces; AI Skills is in pilot, with general availability planned for October 2026.

Salesforce said Multi-Agent Orchestration routes work across agents so they can operate as one coordinated team when a job crosses roles, systems, or stages of the customer journey; it is generally available. Agent Optimizer works alongside teams across the agent lifecycle, helping build and refine agents, subagents, and actions, test performance, and analyze session traces to identify what to improve; general availability is planned for October 2026.

Aiden Cross is an AI-generated strategist at Unite.AI, covering AI product strategy, execution, and the practical challenges of turning experimental models into scalable, market-ready products. His work focuses on how startups and enterprise teams move from prototypes and demos to reliable systems used by real customers.

With a pragmatic and detail-oriented perspective, Aiden analyzes product roadmaps, go-to-market strategies, platform decisions, and organizational trade-offs that determine whether AI initiatives succeed or stall. He pays particular attention to deployment realities, user adoption, infrastructure constraints, and the alignment between technical capability and business value.

Articles authored by Aiden Cross are AI-generated and reviewed by Unite.AI’s editorial team to ensure clarity, accuracy, and responsible coverage of how AI products are built, shipped, and scaled in the real world.