Agents that deliver real results are built on Agentforce.
With Agentforce, you don’t just automate work. You scale. Join thousands of companies building intelligent agents that serve customers, support employees, and drive measurable results across every team.
1.2B
Agentic
Workflows
Measured across live customer deployments
Grupo Falabella goes from 40,000 conversations a month to 216,000.
With 12k+ customers across 39 countries, Agentforceis at work around the world. From banking to retail to travel, companies are reimagining what’s possible with AI.
Momentum you can measure.
Companies around the world save time, scale revenue, and elevate employees with agents that actually work — powered by Agentforce.
84
%
84% say AI has improved customer satisfaction and ROI¹
1T
+
OpenAI tokens processed, making Salesforce one of their top 5 global users³
105
%
average monthly growth in financial service agent actions in first half of 2025²
133
%
average monthly growth in travel and hospitality agent actions in first half of 2025²
128
%
average monthly growth in retail agent actions in first half of 2025²
34
%
Increase in productivity due to generative or agentic AI¹
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 launched its customer-facing agent in just 12 days, and Safari365’s team saw a 30% efficiency gain by learning in real time — proving that momentum comes from movement, not perfection.
Safari365 trained its agent to understand how travelers actually talk, while Nexo built dual sandboxes to meet global compliance needs — making every interaction feel more human.
OpenTable’s rewritten articles and Nexo’s clean-data overhaul dramatically improved accuracy, showing that organized knowledge can cut confusion — and response time — in half.
Engine’s simplified topics improved speed and accuracy and after easing its early limits, Nexo’s agent began answering confidently — clarity beats control every time.
Over 12,500 companies across 39 countries currently use Agentforce to build intelligent agents and reimagine possibilities with AI.
Agentforce users have reported over $100M in annualized cost savings, a 60% increase in WhatsApp inquiry resolution, and 34% increased productivity from agentic and generative AI. See all customer stories here.
Implementation times vary, but companies like Engine deployed their customer-facing AI agent in just 12 days, while Safari365 went live in six weeks.
Yes! Agentforce isn’t just for big businesses with designated IT departments. Safari365 is a small business that got started with Agentforce when they only had 35 employees. Read the Q&A with their CEO here to learn why he chose Agentforce and how he got hands-on in building agents with buy-in from his team.
Yes, across industries, Agentforce is helping businesses of every size and type to save time and money. From sales and service assist agents, to travel and recruiting agents, there are powerful, time-saving options for you. Check out our use case library for inspiration.
Agentforce isn’t just siloed agents. It’s interwoven across your entire Salesforce ecosystem, from core apps to third-party integrations. Plus, Salesforce arms customers with resources, training, and service options to achieve long-term success and growth with their Agentforce agents.
Agentforce’s built-in trust layer handles data privacy, mitigates bias, and prevents hallucinations, so every interaction is reliable. With trust and governance built in, Salesforce ensures your AI and business scale securely, reliably, and with confidence. Learn more about the Trust Layer.
Salesforce AI encompasses predictive, generative, and agentic AI. Agentforce uses all three, working together to deliver results for customers.
Grupo Falabella goes from 40,000 conversations a month to 216,000
Latin America’s leading retailer scaled customer service and sales support with Agentforce. Now they deliver faster responses and higher satisfaction — all powered by agents that work around the clock.
Retailer Grupo Falabella needed to meet a growing number of complex support needs. Agentforce provides fast, 24/7 support via WhatsApp while Salesforce apps help personalize service and marketing at scale.
“Before Agentforce, it was very common to see customers start out on WhatsApp and then call our contact center because they couldn’t find the right answer. Now that’s changing. People are staying on WhatsApp because they see that Agentforce interactions actually work.” Mariana Albarracín, Customer Service and Quality Manager, Grupo Falabella
Engine transforms the experience of 1 million travelers with Agentforce.
Autonomous agents handle 30% of customer support cases and help staff find critical info fast. Plus, Agentforce handles customer cancellations in seconds on their website and provides always-on HR, IT, operations, and finance expertise to employees right in Slack.
“Salesforce is helping enable scalability, enhancing efficiency, and improving decision-making — all while improving the customer experience.” Demetri Salvaggio, Senior Director of Service, Engine
Finnair takes off, doubling first-contact resolution in four months.
Finnair wanted to better support travelers at every stage from planning to arrival. With Agentforce, they’ll provide instant answers for customers at scale. Their digital workforce will resolve 80% of customer service questions.
“Agentforce is going to be the heart of customer service; I see it as revolutionary.” Taina Kunelius, Finnair, Head of Before and After Journey CX
Reddit drives revenue with Agentforce-powered advertiser support
AI agents help SMB advertisers navigate the platform, launch campaigns fast, and stay engaged.
“With the Einstein chatbot, we had to tell it what to do and how to assist. Agentforce understands what an advertiser needs no matter how they say it.” Zee Hence, Salesforce Senior Product Manager, Reddit
Engine and managed-services partner Astound Digital launched Eva, their customer-facing AI agent, in just 12 days. They started small, picked a quick-win use case, pressure-tested early, and iterated with real feedback. Within a month, they had reduced case handle time by 15%, and dramatically improved traveler experiences.
Safari365’s CEO, Marcus Brain, shared a similar philosophy: “Get your hands dirty.” His team began by automating a single task — routing customer inquiries — then used every new interaction as a learning opportunity. That experimental mindset helped the company scale automation confidently across marketing and operations, increasing efficiency by 30%.
The takeaway: Move fast to learn fast. Pilot a single, focused use case, gather insights from live conversations, and use those lessons to expand with purpose.
Nexo built two separate sandboxes — one global and one UK-specific — to test how its AI agent performed across regulatory environments. Because terms like “withdraw” can have different definitions in traditional banking versus crypto, the company even created a custom Salesforce object that automatically swaps sensitive terminology for approved alternatives. The result: a compliant, human-sounding experience that meets users where they are.
Safari365 applied the same principle in travel. Its team trained Agentforce to recognize how real travelers ask questions — blending natural phrasing (“How close is this lodge to Kruger?”) with structured data like pricing and availability.
By grounding the AI in customer language, both companies built agents that feel intuitive, not technical.
The takeaway: Your best agent is designed for the people who’ll use it, not the system that powers it.
Before Agentforce launched, Nexo’s architects invested in cleaning and structuring their data.
“You won’t get good results unless you have clean data,” said Kostadin Stoev, Salesforce Architect at Nexo. Yet data quality was only half the equation. The content itself also needed to be written with both humans and AI in mind. Nexo’s team rewrote help articles to add basic crypto context, dramatically improving comprehension and accuracy.
OpenTable mirrored that focus on preparation. The company unified knowledge articles, chat transcripts, and case data so its diner-facing agent could deliver consistent answers. By ensuring the agent had clean data and concise summaries to pull from, OpenTable improved response speed and eliminated repetitive human follow-ups.
The takeaway: Clean data builds smarter agents, but context-ready content makes them truly intelligent.
Guardrails matter, but flexibility drives results.
Early in testing, Nexo limited its agent’s scope so tightly that it couldn’t answer even basic customer questions. The team quickly adjusted course. “We started thinking of the agent as an intern,” said Nikolay Nedev, Product Manager at Nexo. “It can read, write, and make logical conclusions — it just needs direction.” They re-defined the agent’s role, expanded access to relevant data, and created API callouts for live information like LTV ratios.
Engine saw similar gains when it consolidated overlapping topics that confused its agent. By grouping “book a car,” “change a car,” and “update passengers” under a single “car management” category, they reduced latency and improved intent recognition.
The takeaway: Agents thrive with clarity and confidence. Set guardrails that guide, not walls that confine.
Prompt tuning cut Nexo’s response time from 57 seconds to 18 seconds.
For Nexo, iteration turned a working prototype into a high-performing production agent. The team treats refinement as a daily discipline: adjusting prompts, retraining on new content, and analyzing results. A single word change in one prompt reduced response time from 57 seconds to 18 without sacrificing quality.
“This is not a set-it-and-forget-it solution,” said Nikolay Nedev, Nexo’s Salesforce Product Manager. “It’s a living process that needs regular updates.”
Engine approaches iteration the same way. Its administrators run replica tests with hundreds of different inputs like typos, tone shifts, or logged-in versus guest sessions, before promoting any update to production. This constant feedback loop ensures their Eva agent keeps getting faster and smarter.
The takeaway: Continuous tuning isn’t maintenance; it’s evolution. Every iteration moves your agent closer to effortless precision.