Operationalising Agentforce: From AI Promise to Practical Value

Treat Agents Like Employees

Agentforce allows you to build both autonomous and assistive agents. They can perform tasks like summarising customer service cases, surfacing insights from CRM data, or retrieving sales documents. However, deploying them effectively requires more than a technical setup.

“You have to think about agents as if they’re team members,” says Boris Naumov, Manager for AI, Data & Analytics at Fluido. “They need access to data, specific skills, and clear instructions. And just like people, they require oversight and iteration.”

A successful agent deployment mirrors onboarding a new hire:

• Knowledge base – The data that powers their responses
• Skillset – The actions and tasks they’re authorised to perform
• Workflows – Instructions and boundaries that guide behaviour
• Feedback – Continuous monitoring to improve performance

As Greg Anderson, Senior Business Advisor and a member of Agentforce Salesforce Partner Advisory Board EMEA at Fluido, puts it: “If you give an employee poor data, no training, and no supervision, you’ll get poor outcomes. The same is true for agents.”

Start Narrow, Scale Smart

While the long-term potential of Agentforce is vast, the best way to begin is with focused use cases that solve immediate problems and build confidence. Examples include:

• Generating knowledge articles after customer service interactions
• Suggesting next-best actions for sales teams based on historic CRM data
• Assisting with document search and retrieval across departments

These kinds of pilots can be implemented quickly, often in days, using Salesforce tools like the Einstein Data Library and Agent Builder. However, success depends on the quality of your data and the clarity of who owns the process.

Let Business Logic Lead the Way

Agentforce isn’t a plug-and-play solution. It should be designed around your actual operations. If your customer service team already logs calls, tickets, and resolutions in Salesforce, then an agent can be genuinely helpful. But if key interactions happen offline or go unrecorded, the agent won’t have the data it needs to add value.

“What’s not in the system doesn’t exist to the agent,” Naumov notes. Businesses must take a hard look at where and how data is captured, and ensure that agents are being deployed in contexts where they can make informed decisions.

Monitor, Measure, and Iterate

Deploying an AI agent is not the end of the journey– it’s the beginning. Agents, like employees, need to be evaluated and refined over time. Organisations should track their accuracy, user satisfaction, and impact on KPIs.

Additionally, tools like Salesforce’s Data Cloud and MuleSoft integrations make it possible to expand agent capabilities as your needs evolve, pulling in external data or triggering actions in third-party systems.

Greg Anderson adds, “Scalability doesn’t come from doing everything at once. It comes from doing something well and then repeating it with purpose.”

Looking Ahead

As generative AI evolves, Agentforce will become even more powerful. Upcoming innovations– like agents with multimodal capabilities (image, voice, and video), dynamic user interfaces, and context-aware automation– promise to push the boundaries of what AI can do within business environments.

However, future capabilities are only as valuable as today’s foundations. Getting started with well-scoped use cases, strong data, and a thoughtful operational framework remains the best approach.

Turning Potential into Performance

Agentforce has the power to transform how businesses operate, but only when deployed with intent. Companies that treat agents as an extension of their workforce, invest in the proper data infrastructure, and align AI with their business goals will see the greatest return.

If you’re ready to begin, Fluido can help, from strategy and design to implementation and governance.

If you would like more insight, you can explore expert commentary in the Fluido Moments podcast, where Boris Naumov and Greg Anderson share real-world lessons on deploying AI within Salesforce ecosystems.