{"id":13259,"date":"2026-03-24T08:00:00","date_gmt":"2026-03-24T15:00:00","guid":{"rendered":"https:\/\/www.salesforce.com\/?p=13259"},"modified":"2026-03-26T02:29:18","modified_gmt":"2026-03-25T15:29:18","slug":"ai-lessons-building-enterprise-agents","status":"publish","type":"post","link":"https:\/\/www.salesforce.com\/au\/news\/stories\/ai-lessons-building-enterprise-agents\/","title":{"rendered":"Applied AI: Lessons from Building Agents in the Enterprise"},"content":{"rendered":"\n

There\u2019s a lot of noise right now about AI agents<\/a>. Every vendor has one. Every keynote promises them. But very few organizations have actually shipped agents that work in production, at scale, against real business outcomes. At Salesforce, we have. And what we’ve learned in the process is that most of the industry is thinking about this wrong.<\/p>\n\n\n\n

This paper isn\u2019t a product pitch. It’s a set of hard-won lessons from building and deploying AI agents inside one of the largest enterprise software companies in the world. As Customer Zero<\/a>, we use our products first, test them in real-world scenarios, tune them, and then build those lessons directly back into the platform. We\u2019ve made mistakes internally so that our customers don\u2019t have to. Every insight in this guide is the result of a disciplined strategic plan refined through experimentation and necessary trial and error.<\/p>\n\n\n\n

These lessons apply whether you’re running a five-person team or a thousand-person organization. The principles are the same. The mistakes are the same. And the opportunity, if you get it right, is enormous.<\/p>\n\n\n\n

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1. AI Agents Aren\u2019t Software<\/strong><\/h2>\n\n\n\n

This is the first and most important thing to internalize. Software is deterministic. You write a function, you pass it an input, you get the same output every time. That’s what makes it reliable. It’s also what makes it limited.<\/p>\n\n\n\n

Agents are fundamentally different. They have their own reasoning capabilities. They interpret context. They generate responses that vary, and that variation isn\u2019t a bug\u2014it’s the feature. It’s the very thing that makes them useful in complex, ambiguous, real-world business situations where rigid logic breaks down.<\/p>\n\n\n\n

The problem is that most enterprise leaders are still treating agents like software deployments. They expect deterministic behavior. They expect to configure it once and walk away. And when the agent does something unexpected, they call it a failure. That’s like hiring a new analyst and being surprised when they ask a clarifying question.<\/p>\n\n\n\n

The mental model shift is this: Managing agents is closer to managing employees.<\/strong> The better you guide and coach them, the better they perform. Clear, well-written briefs produce better work. Active monitoring catches mistakes early. Calibration through examples \u2014 showing what good looks like and what bad looks like \u2014 drives improvement over time. And when a single agent is overloaded with too many tasks, the answer isn’t to push harder. It’s to hire another one.<\/p>\n\n\n\n

The best agent builders we’ve seen operate like great managers. They design work assignments across a team of specialized agents, coordinating them the way a competent leader coordinates an organization. That’s the unlock.<\/p>\n\n\n\n

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2. Think in Terms of \u2018Jobs to Be Done,\u2019 Not \u2018Jobs\u2019<\/strong><\/h2>\n\n\n\n

There’s a persistent myth that you can build one universal agent to replace an entire human role. You can’t. Not today. And probably not for a long time. The roles humans play in an enterprise are composed of dozens of discrete tasks, each with its own context, judgment requirements, and edge cases. Trying to automate an entire job with a single agent is a recipe for mediocrity across the board.<\/p>\n\n\n\n

The breakthrough comes when you break the job down into its component tasks \u2014 the specific “jobs to be done” \u2014 and build agents that master each one.<\/p>\n\n\n\n

Take our Sales Development Representative (SDR) team as an example. SDRs are our front line for connecting with prospective customers. In their working day, they do many things: They schedule meetings with prospects, share product information, run initial qualifying conversations, hand off the most promising leads to account teams, log their calls, and create sales opportunities inside Salesforce.<\/p>\n\n\n\n

When we built our Engagement Agent, we focused on specific, measurable tasks for a few scenarios that a human SDR did in their job. The goal was to provide our SDR team with more capacity to help them meet their quota by engaging with more customers. We did this by having the Engagement Agent keep following up when normally the SDR would need to stop to move on to other customers. We had the agent reach out to prospects that our human SDR teams didn\u2019t have capacity to speak with to engage with the customer until they were ready to book a meeting. We had the agent help customers with their initial product questions so that they would be ready to connect with our sales teams. <\/p>\n\n\n\n

The results spoke for themselves. Our initial Engagement Agent pilot generated more than <\/em><\/strong>$120 million annualized pipeline<\/em><\/strong> <\/em><\/strong>in just the first few months of operation. Not by trying to be an SDR but by doing specific SDR tasks exceptionally well. Our SDR team couldn\u2019t have hit their targets without the Engagement Agent.<\/p>\n\n\n\n

We didn\u2019t get it right overnight. In our first version, the agent could beat only the bottom 10% of our human SDRs. To improve, we looked at what our top sellers do: how they write the best emails, create the right headline, and deliver a call to action. We measured how those actions led to more customer appointments and more revenue. Through consistent measurement and experimentation, we made the Engagement Agent better and better. Once we knew what worked, we built those insights into the product. Today, that agent can beat 90% of our sellers in those tasks. We built the Engagement Agent for ourselves, made it great through our own experience, and then delivered that refined version for our customers.<\/p>\n\n\n\n

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3. Measuring Agent Competency<\/strong><\/h2>\n\n\n\n

If you accept that agents are more like employees than software, you have to measure them like employees too. That means evaluating competency on a specific task, not running generic benchmarks that have nothing to do with your business context.<\/p>\n\n\n\n

Think about a call center. Entry-level call center employees work from fixed scripts. They’re effective within narrow guardrails, and they’re measured on adherence and resolution within those bounds. As they advance in their expertise, they earn more flexibility. They can deviate from the script. They can handle more-complex customer situations. Their competency is measured by their ability to resolve increasingly difficult cases while maintaining quality standards.<\/p>\n\n\n\n

AI agents should be evaluated with this same comparative rigor. An agent that’s new to a task should be tightly constrained, with every output reviewed. As it demonstrates competency \u2014 resolving simple tickets accurately and consistently \u2014 you give it more latitude. Over time, the best agents graduate from handling simple inquiries to resolving complex, multistep customer cases that would have previously required senior human intervention.<\/p>\n\n\n\n

Applying this level of rigor to our AI agents served as a mirror for our own organization, revealing that we must measure agent success against a human baseline. In our own journey of deploying agents, we found we often had more granular data on the agents than on all of the tasks humans do in their jobs. By rigorously quantifying what excellence looks like for the work our teams do today, we can measure whether an agent is actually delivering on its mission.<\/p>\n\n\n\n

This isn’t theoretical. It’s how we’re building competency models inside Salesforce right now. The metric that matters isn\u2019t whether the agent is “smart.” It’s whether the agent can do this specific thing, reliably, at a level that meets or exceeds human performance.<\/p>\n\n\n\n