Most people we talk to share a common challenge: they recognize the potential benefits of Agentforce and are eager to explore but are uncertain about how to proceed. Many struggle to bridge the gap between Salesforce’s AI vision and the tangible value it can deliver to their organization. These conversations often highlight recurring themes: determining the most effective Agentforce use case for the business, assessing the effort required to bring it to market, and understanding the financial implications. Effective Agentforce use cases Different types of agents available with Agentforce. Salesforce’s Agentforce offers businesses customizable AI agents that automate routine tasks such as order tracking, customer support, and troubleshooting. These agents integrate seamlessly into various communication channels, improving operational efficiency and delivering personalized service. As AI handles simple tasks, it frees human agents to tackle more complex issues. The platform also uses AI for knowledge assistance and can escalate complicated queries to human agents when necessary, ensuring that businesses can provide the right support to their customers. For more details, check out Salesforce’s Agentforce Use Cases. At N28, we have successfully assisted clients in a variety of impactful use cases to get started on Agentforce. Here are some examples: Sales and marketing use cases Generate quotes: Where our AI agents interact with Sales staff to produce customized quote PDF documents for specific customers. AI Account manager: Recommend products based on insights from current orders and market data, optimizing sales strategies. Operational use cases Validate purchase orders: Automating tasks like price checks, identifying complex offer pricing, and initiating rebate calculations Admin AI: Automates routine administrative tasks within Salesforce orgs, saving time and reducing manual effort. Technical framework behind Agentforce Agentforce’s architecture is built on four fundamental components that work in harmony to deliver intelligent, secure, and effective agent-based solutions: Data for AI: Foundation begins with comprehensive data integration capabilities, supporting both structured and unstructured data from internal and external sources. This robust knowledge base serves as the fundamental building block that powers agents’ decision-making and operational capabilities.  Large Language Models (LLMs): Leverages state-of-the-art LLMs to transform raw data and analytics into natural, conversational interactions. This technology enables fluid communication between users and agents, making complex data accessible and actionable through intuitive dialogue. Agent Core: A sophisticated suite of tools for designing customized agent experiences. This framework enables organizations to: Define Topics: Create precise agent parameters through detailed instructions, descriptions, and clearly defined operational scope Configure Actions: Establish specific, executable tasks and operations within each defined topic area Trust framework: A comprehensive security and governance layer that ensures controlled access management, secure channel operations, compliant agent interactions and protected data handling. This integrated architecture ensures that Agentforce delivers not just powerful AI capabilities, but does so within a secure, controlled, and trustworthy environment. Other common questions Data Cloud: Agentforce leverages the power of Data Cloud while maintaining implementation simplicity. While Data Cloud activation is a prerequisite for Agentforce deployment, organizations can begin operations using solely their CRM data—eliminating the need for extensive Data Cloud configuration at launch. Key architecture points: Data Cloud houses the Vector Database infrastructure required for RAG (Retrieval Augmented Generation) implementations Initial deployment can operate efficiently with existing CRM data Flexible architecture allows for gradual expansion of Data Cloud functionality This approach enables rapid deployment while preserving the option for enhanced Data Cloud integration as organizational needs evolve. LLM options: Out-of-box support: Pre-configured integration with select OpenAI models Pre-configured integration with select Anthropic models Flexible deployment: Support for custom LLM integration (“bring your own model”) Maintain control over your preferred large language models This flexible architecture allows organizations to leverage either pre-integrated models or implement their preferred LLM solutions based on specific requirements. Financial overview License cost: Unlike traditional user licensing, Agentforce employs a conversation-based pricing structure, charging $2 per agent interaction, with potential volume discounts. This model shifts costs from upfront licensing to usage-based fees, potentially reducing initial expenses but increasing costs with higher usage. There might be additional costs associated with Data Cloud credit usages and LLM model usages. With this on-demand pricing model, organizations must evaluate the trade-off between automating tasks through Agentforce and the associated per-interaction fees to determine the most cost-effective approach. N28 implementation cost: Staged implementation approach. Understanding that AI adoption requires careful consideration, we’ve designed our AI services to support a measured, step-by-step implementation journey. Click the link below to review our Agentforce offerings. Check Out Our Agentforce Offerings Here.

Dreamforce ’24 was an outstanding event for Salesforce AgentForce. I gained valuable insights into Salesforce strategy, engaged in productive client meetings, and, most importantly, had meaningful conversations with peers about what customers are seeking in Data Cloud and Gen AI. For large companies with established data engineering capabilities and existing investments in business intelligence, what’s the value in further investing in Data Cloud and AgentForce? And for smaller companies, is AgentForce truly affordable? So, what is Agentforce? Salesforce’s solution empowers clients to build autonomous agents on top of their existing Salesforce platforms. It goes beyond traditional bots with integrated GenAI capabilities and surpasses standard data warehouses by grounding responses in real-time data. The standout feature is the intelligent learning engine, Atlas, which identifies the next-best action based on ongoing interactions. For me, the best part of AgentForce is its no-code/low-code functionality, enabling faster use-case deployment and significantly reducing time-to-market. Here is a summary of all the components that make up AgentForce: Why would Agentforce need Data Cloud? Data Cloud, on the other hand, serves as a microcosm of broader business intelligence capabilities—typically offered by comprehensive platforms like Snowflake or Databricks—integrated directly within the Salesforce ecosystem. It provides out-of-the-box extract, transform, and load (ETL) capabilities for data residing both within and outside of Salesforce organizations, laying the groundwork for advanced in-platform analytics. The platform’s zero-copy capabilities address the persistent data duplication challenge, ensuring data integrity and security. AgentForce relies on Data Cloud to deliver transformed business insights, enhancing response accuracy and relevance by incorporating contextually relevant information, regardless of data location. Here is a top-down view of how it works: Should larger companies adopt Agentforce? Absolutely—and for a straightforward reason: speed to market. For companies that prioritize building from the ground up and have substantial engineering resources, creating a fully customized AI capability in-house is achievable and may lead to standout, GenAI-driven innovations—you might be the next technology product unicorn start-up on Gen AI. However, for most companies where rapid market deployment is essential to maintain a high standard of user experience for both internal and external users, AgentForce offers a streamlined path to implementing GenAI use cases on the business front lines. This empowers agents to enhance sales and service team support with minimal lead time. Should smaller companies venture into Agentforce? Unequivocally, yes. With 45% of the U.S. population already using Generative AI, these capabilities are becoming a necessity. It’s only a matter of time before customers and employees demand GenAI features in every platform they interact with. While building in-house GenAI expertise and infrastructure can be costly—not to mention the ongoing operational expenses—AgentForce offers a compelling solution. Its on-demand pricing model, combined with a no-code/low-code platform, significantly mitigates investment risks, while the faster time-to-market makes it an obvious choice for smaller companies. What should the roadmap for Agentforce look like? This is not an easy question to answer, but there are several clear and established approaches for enabling the organic adoption of this new technology. To begin with, GenAI use cases focused on workflow automation represent the low-hanging fruit—such as automating customer interactions for service requests or automating lead qualification, for example. Future phases should concentrate on creating entirely new workflows driven by GenAI: replacing manual, time-consuming processes (like navigating through countless clicks) with more efficient, streamlined solutions. Additionally, developing next-best-action use cases will be key in maximizing the potential of GenAI. What is more important than all the tech talk is to STOP procrastinating. N28 is here to help. Use the form below to get in touch regarding your next project. Get In Touch