The Agentforce Specialist Certification is tailored for professionals who want to leverage the capabilities of generative AI within the Salesforce platform. Those in this role are key to implementing and managing Einstein AI solutions, optimizing business processes with the help of advanced tools like Copilot Builder, Prompt Builder, and Model Builder.

This certification is particularly suited for Salesforce Administrators, Developers, and Architects, who utilize both standard AI functionalities and custom enhancements to drive innovation within their organizations.

There are 5 areas of knowledge that are covered by the Salesforce Agentforce Specialist certification.

Agentforce Specialist Topic Weighting Chart

The following are the core topic areas of the  Agentforce Specialist certification and what you’re expected to know:

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Prompt Builder is a tool that allows users to create detailed prompt templates that instruct large language models (LLMs) to produce AI-generated responses. A prompt template consists of a prompt that utilizes various elements like ingredients, guidelines, and grounding.
Prompt Builder offers several types of reusable prompt templates, such as Sales Email, Field Generation, Record Summary, and Flex, that can be configured to meet specific business needs. The Prompt Template Workspace allows users to draft and test these prompt templates.
The Draft with Einstein button allows users to draft emails using a sales email prompt template. Users can click an icon next to a dynamic form field to use a field generation prompt template and populate the field with AI-generated text. In addition, invocable actions and flows provide seamless integration of prompt templates with workflows.

Salesforce users can be allowed to manage and execute prompt templates by assigning the appropriate permission sets. The Prompt Template User permission set can be assigned to users who need to access and run prompt templates. It provides visibility to the generative AI-enabled icon next to a dynamic form field that uses a field generation prompt template.
The Prompt Template Manager permission set can be assigned to users who need to create and manage prompt templates. The Einstein Sales Emails permission set can be assigned to those who need to draft emails using a Sales Email prompt template. Additionally, it is necessary to ensure that users can access all the relevant fields when drafting sales emails.

When creating a prompt template, it's important to consider best practices, limitations, and other considerations. A well-crafted prompt template should be clear, concise, and consistent. Best practices include roleplaying as a character to provide context and including instructions surrounded with triple quotes (""") in a separate section. There are certain limitations to be aware of. The User and Organization related lists cannot be used as merge fields, and the Activities related list is not supported for objects like Account and Case. Certain numerical limits, such as the maximum number of related list merge fields, also apply to prompt templates. Furthermore, a Lightning record page must be upgraded to Dynamic Forms to use field generation prompt templates, and data grounding must be accurate and complete for the expected results.

When configuring prompt templates in Salesforce, selecting the appropriate grounding technique is essential to ensure accurate and relevant AI-generated content. Grounding techniques connect a prompt template to data sources such as Salesforce records, related lists, Apex classes, and external data. For instance, a record merge field links a template directly to an object field, while a related list merge field is used to ground a template using an object's related list.
A flow merge field can introduce dynamic logic by triggering a flow for a more complex use case. A record snapshot can be used to ground a prompt with data available on the user's page layout for an object. Prompt templates can also utilize Apex merge fields for programmatic use cases and DMO merge fields to use data stored in Data Cloud. Moreover, Retrieval Augmented Generation (RAG) can be used to ground templates with unstructured data, such as knowledge articles, emails, and chat transcripts.

Using Prompt Builder effectively requires understanding how to create, activate, and execute prompt templates. When creating a new prompt template, an AI specialist can specify its type, name, and description. Depending on the selected type, additional fields may be required. The Prompt Template Workspace provides a space to write, preview the resolution and response, and test a prompt. It also allows users to select the model configuration. An edited prompt template can be saved as a new version or new template, but it must be activated to make it available to users. The execution of a prompt templates depends on its type. A sales email prompt template can be run by clicking the Draft with Einstein button. A field generation prompt template can be run by clicking a generative AI-enabled field icon. A flex prompt template can be run using an invocable action, Connect REST API, or Connect in Apex. Consistently generating effective prompts is an iterative process, requiring best practices like testing multiple responses.

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The reasoning engine and the large language model (LLM) play a critical role in executing agent actions based on user requests. The reasoning engine is responsible for orchestrating this process by launching topics and actions, ensuring the right tasks are performed to meet the user's request. The LLM identifies the user's intent, determines the best matching actions in the right order, and generates an appropriate response while maintaining the conversation flow. Additionally, session event logs in the Agent Builder can be accessed to debug and analyze the sessions associated with the execution of copilot actions. Einstein Copilot supports OpenAI GPT-4o for planner service calls. Copilot actions support making calls to other predefined LLMs.

Agents, such as the Agentforce (Default) agent, can leverage standard and custom actions to execute tasks on behalf of Salesforce users. Standard actions are included with agents by default and help users complete common tasks. Custom actions, on the other hand, offer flexibility to tailor the agent’s capabilities to specific business needs. When creating a custom action, an AI specialist can select a Reference Action Type, which can be Apex, Flow, or Prompt Template. Agent actions can be assigned to topics in Agent Builder. Instructions can be specified to define how a custom action should be used during conversations. Properly managing and assigning these actions ensures that agents deliver the most relevant and effective responses to user requests.

To effectively manage and monitor the adoption of an agent, such as the Agentforce (Default) agent, it's important to track user interactions, feedback, and usage analysis. Agentforce Analytics provides dashboards and reports that offer insights into the usage, feedback, and adoption of agents, helping refine their effectiveness. The Utterance Analysis dashboard provides insights into how users engage with Agentforce (Default), their requests, and whether the agent is able to handle those requests. Event Logs in the Agent Builder allow an AI specialist to track detailed logs of user interactions to optimize user experiences and resolve common agent issues.

         Managing Agentforce user security involves configuring permissions and access controls to ensure secure interaction with AI agents in Salesforce. Permissions can be assigned to both agents and users who require access to specific Agentforce functionalities. This is achieved through permission sets, which define access levels based on roles and responsibilities.
To create an Agentforce (Default) agent, users must have the Manage AI Agents and Manage Agentforce Default Agent permissions. For specialized agents, additional permission sets such as Manage Sales Coach Agent are required. An agent user record must be assigned to an appropriate permission set that contains the Agent User license.
Security best practices for Service Agents include following the principle of least privilege, implementing two-factor authentication, and limiting the scope of private actions.

         The Agentforce Testing Center enables efficient testing of agents by allowing a large number of utterances to be evaluated in a single test. This reduces overall testing time and facilitates the quick activation of agents. Users can access the Testing Center in Salesforce Setup and leverage batch testing to execute multiple test cases simultaneously. The testing process involves creating a CSV file with test cases, running the test in a sandbox environment, and analyzing the test results for refinement. The test results display both successful and failed utterances, which helps fine-tune instructions, topics, and actions. Running tests consumes Einstein Requests and Data Cloud credits, and should be conducted in a sandbox to prevent unintended CRM data modifications. The Testing Center is available with the Einstein for Sales, Einstein for Service, or Einstein Platform add-ons and supports up to 10 tests in a 10-hour timeframe and 200 test cases per test.

         Deploying an Agentforce agent from a sandbox to production environment ensures its availability for end-users. This process involves metadata deployment using Change Sets or Metadata API. All the relevant metadata components, such as GenAiPlanner, Einstein Bot, and Bot Version, must be included in the deployment. Additionally, service agents typically require the Embedded Messaging component for deployment to an Experience Cloud site. After deployment, the agent must be activated in the Agent Builder to make it available to users. Finally, stakeholders and end-users must be informed, with proper training and documentation provided.

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            Agentforce Data Libraries enhance AI features by improving accuracy, adding personalization, and building trust in generative AI responses. A data library acts as a structured repository of knowledge that an Agentforce agent can use to provide precise and contextually relevant answers. Data libraries can be sourced from the Salesforce Knowledge base or uploaded files (such as text, HTML, and PDFs), ensuring agents have access to reliable information.
Key features include grounding AI responses with domain-specific knowledge, chunking data for efficient retrieval, and indexing for organized searches. Data libraries also support retrievers, which fetch relevant information dynamically. A data library can be configured to use an organization’s Knowledge base as its data source by selecting Identifying Fields and Content Fields. Specific files can also be uploaded and used as the source of a data library. A data library can be assigned to an agent in the Agent Builder.

           In Data Cloud, Retrieval Augmented Generation (RAG) can be utilized to ground large language model (LLM) prompts with accurate, current, and pertinent information. By retrieving structured and unstructured data from vector databases, retrievers improve the relevance and value of AI-generated responses. The retrieval process involves indexing data for efficient search, adding retrievers to prompt templates, and configuring retriever settings. Default retrievers are created automatically when search index configurations are created, while custom retrievers can be created and customized with filters in Einstein Studio. The configuration panel in Prompt Builder allows fine-tuning retriever settings.

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Agentforce supports AI-driven customer support by building an agent that provides answers based on knowledge articles. By leveraging the Answer Questions with Knowledge standard action, agents can access relevant information from knowledge articles and uploaded files to respond to user queries effectively.
Agent Builder allows adding the standard action to a topic assigned to an agent. The action enables agents to provide accurate and context-aware answers while respecting permissions and sharing settings. The General FAQ topic includes the action by default and can be assigned to agent.

An Agentforce Service Agent can be connected to a digital customer channel, such as a messaging channel exposed on an Experience Cloud site. By setting up Omni-Channel, a Messaging Channel, and Omni-Channel Flows, messaging requests can be routed efficiently to an agent. Omni-Channel Flows help manage inbound and outbound routing of conversations, while an Embedded Service Deployment allows an agent to engage with customers through an Experience Cloud site. Context variables can be used to personalize interactions, and progress indicators keep customers informed about agent activity. Before going live, testing in a test channel ensures proper message formatting. By integrating an agent with a digital channel, an organization can enhance customer engagement and deliver a more responsive, AI-powered support experience.

Agentforce for Service offers various generative AI features that can be utilized in customer service scenarios. Einstein Service Replies for Email and Einstein Service Replies for Chat can be implemented when agents need AI-generated responses to handle customer inquiries efficiently. Service AI Grounding ensures that the responses are grounded in case context or the company’s knowledge base. Einstein Work Summaries can be set up to predict and fill case summaries, issues, and resolutions. Einstein Reply Recommendations is the ideal choice for providing relevant responses to agents in chat and messaging sessions based on the org’s closed chat transcripts. For scenarios involving the creation of new cases, Einstein Case Classification helps by automatically predicting key fields such as Priority, Reason, and Type. Other generative AI features for service include Einstein Article Recommendations, Einstein Bots, Case Wrap-Up, Knowledge Creation, Conversation Mining, and Einstein Next Best Action.

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Agentforce for Sales offers various generative AI features to boost sales teams' productivity and effectiveness. Using Agentforce, an AI-powered assistant, sales reps can generate record summaries, product recommendations, close plans, etc. Sales Emails allows users to send personalized, AI-generated emails to leads and contacts. Call Explorer enables users to extract critical insights from voice and video calls, such as competitor mentions and product inquiries. Call Summaries allows users to automatically generate detailed post-call summaries, which include next steps and feedback. Other key generative AI features include Einstein Coach, Sales Signals, and Automatic Contact Enhancement. Additionally, other Sales Cloud Einstein features, such as Opportunity Scoring, Lead Scoring, and Einstein Forecasting, help optimize sales strategies. The combination of these features empowers sales teams to work smarter, save time, and improve the overall sales performance.

Agentforce Sales Agents enhance sales productivity by assisting sales reps at different stages of the sales cycle. Agentforce Sales Development Representative (SDR) is sales agent that focuses on the top of the sales funnel with the main goal of nurturing leads. It engages with leads, handles outreach, answers questions, schedules meetings, etc. It is ideal for businesses that need automated lead engagement and qualification. Agentforce Sales Coach supports sales reps at the bottom of the sales funnel, offering personalized coaching, deal-specific feedback, insights, and interactive role-playing to help close deals. It is useful for organizations aiming to improve sales performance through AI-driven coaching. The Agentforce (Default) agent includes various topics and actions that help sales reps perform various tasks related to sales. Certain considerations apply to the use of sales agents. For instance, Agentforce SDR supports assigning only leads and can be deployed across multiple channels, including Email and Messaging.