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Agents that deliver real results are built with Agentforce.
Everyone is building agents. We’re measuring what they actually do. 25,000+ companies have built, deployed, and accomplished real results with Agentforce — and the proof is here.
Grupo Falabella scales WhatsApp support 3x, resolving 60% of inquiries autonomously
Engine keeps 1M travelers moving, cutting handle time 15%, resolving 50% of inquiries automatically
Reddit makes advertiser support the front door to revenue, resolving chat inquiries 84% faster.
Built anywhere. Trusted everywhere.
With 25k+ customers across 124 countries, Agentforce is at work around the world. From banking to retail to travel, companies are reimagining what’s possible with AI.
Customers share tips for a successful Agentforce deployment.
Scaling AI that works doesn’t happen overnight. Our customers built it — one prompt, one workflow, one iteration at a time. Here’s what they’ve learned along the way.
Engine consolidated related subagents to prevent confusion.
Nexo made sure their data and knowledge were agent-ready.
Safari365 got hands-on and used rapid iteration.
OpenTable built flexibility into their escalation logic.
Endress+Hauser trained their agent like a new hire.
reMarkable aligned early with their partners and executives.
Agentforce Behind the Build
Starting small led to success across multiple agents. Engine’s approach was simple: start small and move fast. By starting with a single use case, Engine stayed focused, learned quickly, and scaled agents across their business.
For Safari365, leadership’s commitment paved the way to success. Servicing clients 24/7 across global time zones seemed impossible until Agentforce. This company faced hallucinations, missing guardrails, and technical gaps at first, but their hands-on leadership approach turned 15% efficiency goals into an over 30% reality.
Nexo’s key to success? Setting the vision first. Nexo faced every challenge imaginable as early Agentforce adopters: technical debt, data cleanup, and real-time troubleshooting with no playbook. But their clear vision of the future kept them moving forward, achieving 62% case resolution with Agentforce.
reMarkable transformed customer support with an AI agent named “Mark.” Rapid growth and seasonal spikes pushed reMarkable to rethink support. They built “Mark,” an AI agent that delivers 24/7 personalized service and frees their human team for cases that need a human touch.
Healthcare runs 24/7. UChicago Medicine built an AI agent to match. With 2.5 million patient inquiries a year, many arriving after hours, UChicago Medicine turned to Agentforce to close the gap. Learn how they freed their care teams to focus on what only humans can do, while AI handles the rest.
For SharkNinja, great AI started with great data. 250,000 conversations in, SharkNinja knows what it takes to build AI agents that actually work: clean data, thoughtful guardrails, and a team willing to iterate relentlessly. Here's what SharkNinja learned building their personal shopper agent with Agentforce.
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Frequently Asked Questions
Agentforce is in use by more than 25,000 companies across 124 countries, spanning industries including financial services, retail, travel and hospitality, manufacturing, healthcare, and the public sector. These customers range from global enterprises to fast-growing SMB and mid-market companies. Agentforce is designed for organizations at any stage of AI maturity, from those deploying their first agent to those running multiple agents in production at scale.
Agentforce customers report measurable results across the business. In customer service, teams have resolved 50% of inquiries autonomously, reducing cost per case while improving response speed. In sales, customers report faster pipeline generation and higher win rates. In operations, document-heavy workflows are processed faster. Results vary by use case and data readiness, but customers across industries report productivity gains and cost savings after going live.
An Agentic Work Unit (AWU) is one discrete task accomplished by an AI agent — the moment where raw intelligence is converted into real work. Examples include a prompt processed, a reasoning chain completed, or a tool invoked. Salesforce tracks AWUs as a measure of the total volume of work the platform performs on behalf of customers — not just how many users have access to AI, but how much AI work is actually being completed across the entire Agentic Enterprise, from Agentforce to Slack AI.
Deployment timelines vary based on complexity, data readiness, and the number of use cases, but the range is faster than most customers expect. For example, Engine deployed their customer-facing AI agent in just 12 days, while Safari365 went live in six weeks. For more complex enterprise deployments involving multiple agents, integrations, and change management, a typical timeline is 6–12 weeks from kickoff to production.
Yes. Agentforce was designed for organizations of all sizes, including those without dedicated AI engineering teams. Agentforce's no-code interface, out-of-the-box agent templates, and built-in testing tools make it accessible to admins and business owners. For teams that want hands-on help getting started, Salesforce Professional Services and a broad ecosystem of implementation partners offer rapid deployment programs.
Salesforce offers a tiered set of resources to help customers at every stage. Trailhead provides free, self-paced Agentforce training for admins and developers. Salesforce Professional Services offers Jumpstart packages that can take teams from kickoff to a live agent in as little as two to four weeks. For high-complexity or strategic deployments, Forward Deployed Engineers (FDEs) work directly alongside customer teams. Salesforce also offers an active network of certified implementation partners for organizations that prefer to work with a third party. Once live, the Customer Success organization can support ongoing expansion, optimization, and adoption.
Yes. Salesforce enforces a strict Zero Data Retention (ZDR) policy: when Agentforce sends a prompt to a third-party large language model (such as OpenAI or Anthropic), that provider is contractually required to immediately discard both the prompt and the response after processing. Your data is never stored, inspected, or used to train any external AI model. Inside Salesforce, Agentforce operates entirely within the Trust Layer, meaning it respects your existing role-based access controls, field-level security settings, and sharing rules. If a user doesn't have permission to see a field in Salesforce, the agent cannot see or use it either.