

From takeoff to smooth landing: How Engine deploys Agentforce with confidence.
Learn how smart topic design, rigorous replica testing, and close monitoring help them build high-performance autonomous agents.
Learn how smart topic design, rigorous replica testing, and close monitoring help them build high-performance autonomous agents.
Engine is growing fast. Their client services team handles more than half a million requests from travelers per year, often spending valuable time on routine reservation changes that leave less space for the complex, high-touch cases that really require their expertise. At the same time, the sales team grew fivefold in just one year – from 50 to over 250 sellers – adding new demands on HR, IT, operations, and finance as they worked to support both staff and customers.
To continue offering exceptional service as they grew, Engine turned to Agentforce, the agentic layer of the Salesforce Platform. Their first AI agent, Eva, now manages over 30% of customer cases end-to-end — from rescheduling reservations to recommending accommodations based on preferences — cutting handle times and saving millions annually.
For employees, Agentforce in Slack provides instant support across multiple specialized AI agents. Engine’s AI agent named Mae will act as a multipurpose admin expert, streamlining IT requests, HR support, and finance questions such as, “What’s my team’s budget this quarter?” Meanwhile, a second Slack AI agent called Cloe assists the client services team by delivering real-time case research and account summaries, helping reps respond quickly and accurately.
See what Engine learned from deploying Agentforce.
Neither customers, nor employees, will interact with a new Agentforce topic before we’ve tested it about 100 times using different tones, with typos, without typos, and logged in versus logged out.
Sarah MortonSenior Salesforce Administrator, Engine