For more information on these features, click here to download the Technical Release Readiness PDF for July from the Partner Community.

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Build & Test

Enhanced Escalation Experience

Customer Need: Previously, if no human agent was available during an escalation, the AI agent would silently exit the conversation, leaving the customer stranded.

Impact: Customers can now continue their conversation with the Agentforce Service agent even if escalation isn’t possible, retaining conversation context. The escalation topic can be customized.

Official Release Note Configuration

Set Customer Expectations About Wait Time

Feature: Allows checking rep availability before escalating a customer conversation using the “Check Rep Availability for Routing” agent action.

Impact: If no reps are available, customers can be provided with an estimated wait time and given the option to join a queue or try again later, improving satisfaction.

Official Release Note Configuration

Enhanced Testing with Conversation History

Feature: Introduces the ability to include past conversation data in test cases within the Testing Center and Testing API.

Benefit: Improves realism and accuracy of testing scenarios by providing a more comprehensive context for evaluating agent performance.

Considerations: Requires Agentforce to be enabled and at least one agent activated; certain licensing may be required.

Official Release Note

Enhanced Agent Response Evaluation

Implements a scoring system (0–5) in the Testing Center to evaluate agent responses for accuracy and quality.

Benefit: Provides a nuanced understanding of agent effectiveness beyond pass/fail, allowing identification of specific failures and informed adjustments.

Considerations: Access may require specific permissions (e.g., Manage AI Agents, Customize Application) and potential add-on licenses.

Official Release Note Configuration

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Extend in Ecosystem

Use Gemini 2.5 Models

Feature: Adds support for Vertex AI (Google) Gemini 2.5 Pro and Gemini 2.5 Flash as Salesforce-managed models on the Einstein Platform.

Usage: These models can be used for functionality like prompt templates via Prompt Builder, Einstein Studio, and the Models API.

Official Release Note Supported Models

Integration of Anthropic with Agentforce

Feature: Allows selecting Anthropic on Amazon Bedrock as an alternative LLM provider, operating within the Salesforce trust boundary for secure traffic.

Benefit: Optimizes the performance of the Atlas reasoning engine.

Limitations: May have better performance and latency with OpenAI, supports only English, and lacks multimodal features.

Access: Requires contacting a Salesforce account representative for licensing.

Official Release Note Documentation

Geo-Aware Routing for Agentforce & Einstein

Feature: Directs LLM requests to the nearest OpenAI or Azure OpenAI instances based on the Einstein instance’s location.

Benefit: Enhances response times and minimizes latency, especially for international users, by no longer routing all requests through the US.

Details: Uses GPT-4 Omni for reasoning engine calls; US requests go to local OpenAI, other regions to the closest Azure OpenAI.

Access: Should be auto-enabled for most deployments, but some add-on licenses may be required.

Official Release Note Documentation

This round of release updates covers noteable features from April 2025 and May 2025. At the time of this write-up, the release for May was still in progress which means you may not see all of these features in your customer's accounts until early June.

Agentforce Enhancements: Agentforce Employee Agent (AEA) streamlines employee interactions by automating workflows across multiple platforms including Lightning Experience, Slack, and Salesforce Mobile. This is an upgrade to the existing "Default" agent. Other noteable enhancements include new Test Metrics in the Testing API and the ability to connect Agentforce Service Agent to email.

Prompt Builder Enhancements: Prompt Builder now supports multi-modal AI capabilities by allowing users to incorporate files, such as images and PDFs as well as many other new enhancements including new injection and toxicity detection for the Einstein Trust Layer.

Data Cloud Enhancements: Agentforce Data Libraries now support Web Search and also offer real-time progress updates for the build process. Additional enhancements for RAG support have also been released.

MuleSoft Enhancements: MuleSoft MCP Support provides a unified framework for embedding AI capabilities into existing business processes, driving operational efficiency and improved user experiences.

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Agentforce

Automate Employee Workflows with Agentforce Employee Agent

Agentforce Employee Agent (AEA) streamlines employee interactions by automating workflows across multiple platforms including Lightning Experience, Slack, and Salesforce Mobile. Its natural language interface, coupled with pre-built templates, allows for quick setup and immediate deployment of agents tailored for specific tasks. AEA operates within user context, ensuring responses are role-based and appropriate for the specific user. This capability significantly reduces manual effort when addressing standard topics and actions, thereby accelerating task completion.

The Employee Agent can assist in providing access to company knowledge, performing automated tasks, and streamlining inter-departmental workflows. Its design supports the creation of multiple agents within an organization, overcoming previous limitations of a single agent framework. In addition to enhancing productivity, AEA brings a robust integration with Slack, where agents can be invoked using simple @ mentions. This functionality turns agents into collaborative partners, leveraging conversational context to deliver intelligent responses based on previous dialogues and available data. The framework ensures data security by restricting content access based on user roles and permissions.

Overall, the Agentforce Employee Agent represents a significant enhancement in facilitating employee productivity and efficiency within the Salesforce ecosystem.

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Improve Agent Quality with New Test Metrics in the Testing API

Enhance your evaluation of agent performance with newly added metrics in the Testing API. This update introduces response-quality metrics such as coherence, completeness, conciseness, and response latency. These metrics provide invaluable insights into agent output, enabling more targeted optimization efforts.

• Coherence evaluates the logical consistency of agent responses.
• Completeness measures whether responses fully address user queries.
• Conciseness assesses the brevity and relevance of responses delivered.
• Response Latency tracks the time taken for an agent to respond, thus impacting user experience.

By leveraging these metrics, teams can identify specific areas for improvement, facilitating a data-driven approach to agent training and performance enhancement. The process for utilizing this feature remains straightforward. Create tests using the Metadata API to configure and deploy across different orgs. Test execution can be performed via the Connect API, allowing for streamlined assessment workflows. This enhancement is designed for optimal use within sandboxes of Lightning Experience, available to organizations with Agentforce enabled and at least one active agent.

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Connect Agentforce Service Agent to Email

Agentforce Service Agent now supports autonomous email responses, allowing it to directly engage with customer inquiries via email. This feature enables seamless interaction, especially in scenarios like tracking inquiries, where the agent can provide vital information such as estimated delivery dates and tracking numbers directly sourced from the Agentforce Data Libraries or Salesforce Data.

The structure of the email is defined by a customizable template, while the content is driven by topics, actions, and instructions configured for the Agentforce Service Agent. Control over the email communications can be managed via an Omni-Channel flow, ensuring timely responses or utilizing a basic alternative if preferred. Furthermore, activities and metrics related to emails sent by Agentforce can be tracked using a custom report.

These emails will also appear in the case feed, similar to traditional case emails, maintaining the workflow integrity. It is essential to complete preliminary setup tasks for successful activation, including enabling flex credits billing, creating the Agentforce Service Agent, configuring Email-to-Case, and ensuring compliance with messaging standards. When a customer receives an email response from Agentforce, the Sender field displays the designated Email-to-Case routing address. Each email includes a custom legal disclosure indicating that the response was generated using AI, along with an option for customers to escalate their inquiries to a human representative if desired.

This integration not only streamlines communication but enhances operational efficiency in managing customer interactions effectively.

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Prompt Builder

Prompt Builder now supports multi-modal AI with file inputs like images and PDFs, enabling richer outputs for tasks like product descriptions, troubleshooting, and document analysis. The new step-by-step interface simplifies building, testing, and deploying prompts with clear data handling.

Multi-Modal AI with File Inputs in Prompt Builder

Prompt Builder now supports multi-modal AI capabilities by allowing users to incorporate files, such as images and PDFs, directly into their prompt templates. This advancement enables large language models (LLMs) to leverage visual data and document structures alongside traditional text inputs, enhancing the quality and context of the output generated. Key use cases include:

Enriched Product Descriptions: By combining product images with textual metadata, users can automate the generation of compelling product specifications that accurately capture visual elements, improving engagement and search visibility.

Enhanced Troubleshooting: Technical support can utilize visual context from images, such as screenshots, to provide more precise recommendations. For instance, an LLM can interpret error messages in a "Blue Screen of Death" scenario, leading to tailored advice based on the visual input.

PDF Analysis and Comparison: Users can build automation flows that interpret contract PDFs, identifying discrepancies against existing Salesforce records.

File Object and Notes & Attachments: Users can integrate file inputs either through File (ContentDocument) records for technical integrations or via the Notes & Attachments related list for simpler setups (More on how to use it below :point_down: )

This multi-modal capability aims to refine AI responses and enhance workflow efficiency by leveraging rich contextual insights from user inputs, fundamentally transforming how agents and workflows are developed within Salesforce.

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Step-by-step visuals in Prompt Builder

Salesforce’s new Prompt Builder interface introduces a more intuitive and streamlined experience for creating and managing AI prompts. Designed with a step-by-step visual layout, it guides users through each phase of the prompt-building process, from initial creation to testing and activation. The new interface simplifies the experience, providing clear sections that illustrate how the prompt is constructed, how organizational data is integrated, and what the final response from the large language model (LLM) will look like.

With this update, users can easily ground their prompts in real-time Salesforce data by inserting merge fields using the Insert Resource tool. The Resolved Prompt view allows users to see exactly how test inputs are merged while ensuring that any sensitive organizational data is properly masked before being sent to the LLM. This transparency not only enhances trust but also makes it easier to troubleshoot and refine prompts for better performance.

The updated interface also offers a comprehensive preview of the LLM’s response, helping users evaluate tone, clarity, and accuracy before deploying. Built-in controls like Template & Preview Settings enable users to fine-tune the prompt’s behavior and output. Once satisfied, prompts can be saved and activated for seamless use across Salesforce apps such as Sales Emails and Lightning pages. This new visual and guided approach significantly lowers the barrier to leveraging generative AI, making it accessible even to users with minimal technical background.

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Agentforce and Data Cloud

Agentforce Data Library now supports Web Search, enabling agents to access real-time public information to enhance their responses. Users can easily create a Web Search data library, with progress tracking to know when it’s ready

Agentforce can now use Web Search

With the new capability to integrate web search into Agentforce via the Data Library, agents can now enhance their information retrieval processes. This feature allows agents to perform real-time web searches, enabling them to access a broad spectrum of publicly available data for user inquiries.

To configure a data library for web search, create a new Data Library and select Web Search under Data Type. Once activated, the system automatically generates essential components, such as data streams and a search index, which can be managed within Data Cloud. Monitoring the readiness of the library through the Setup is crucial, as agents can only leverage this functionality once the status updates accordingly.

You can associate this web search data library with existing or new agents. Furthermore, defining a General Web Search topic will enable agents to clarify their capabilities. This topic instructs agents to use only publicly available information and avoid sensitive or personal data retrieval. Important guidelines include asking for clarifications on vague queries, highlighting conflicting information from multiple sources, and ensuring that the information provided is not harmful or biased.

To utilize this feature effectively, remember to remove the existing General FAQ topic, as only one topic can use the Answer Questions with Knowledge action that facilitates this functionality. Once set up, agents are ready to provide enhanced, informed responses powered by real-time web search capabilities.

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ADL Real-time Updates

Agentforce Data Library now offers real-time progress updates for the build process, giving users clear visibility into when a library is being built and when it’s ready for use.

With clear progress indicators, users are informed when the system is actively constructing the data library. During this time, the assigned agents do not utilize the data from the library in testing or user conversations, ensuring accuracy and preventing reliance on incomplete datasets.

Once the build process is complete, a clear confirmation message informs users that the data library is ready for use. At that point, the library can be tested and deployed with agents in live environments.

This feature helps streamline the setup process and provides assurance that agents are working with fully prepared and accurate data.

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Use files stored in Salesforce to ground Agentforce (Data Cloud Content Document Ingestion)

Salesforce Data Cloud now supports ingesting and analyzing unstructured data like PDFs and text files, stored as ContentDocument objects in your org. This allows you to transform documents into meaningful insights by indexing them for search, harmonizing them with structured data, and powering AI experiences like generative answers and agent responses.

With this feature, you can go beyond answering questions based solely on knowledge base articles. You can personalize responses for each customer based on documents attached to their account—such as specific contracts, insurance policies, or any other relevant content—making interactions more accurate and context-aware.

Add an Ensemble Retriever to a Prompt Template

Ensemble retrievers allow for sophisticated querying in LLM prompt templates by aggregating data from multiple sources. This feature enhances the grounding of LLM prompts with precise and relevant knowledge, improving the overall response quality.

When utilizing an ensemble retriever, the system runs individual retrievers in parallel, processing each one’s search independently. The results are then combined into a single list which is reranked based on relevance to ensure that only the most pertinent information is returned, optimizing the efficiency of the prompt or agent interaction.

To incorporate an ensemble retriever in Prompt Builder, initiate by creating or editing a prompt template. In the Prompt Template Workspace, insert the retriever by selecting Einstein Search | Ensemble Retriever under the Insert Resources option. This will display the available ensemble retrievers for selection. Customization options enhance the functionality of ensemble retrievers.

Users can define specific search text (agent input) to narrow results, select desired output fields to ground prompts effectively, and set a maximum number of retrieved results.

This capability ensures that prompts leverage the most relevant and contextual insights available, significantly improving the response quality in various applications.

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Customize Retriever Output with Prefilters

Prefilters in Prompt Builder enhance the specificity and relevance of data returned by the Einstein Search retriever in prompt templates. These prefilters allow technical users to set conditions that constrain the retriever’s search scope, ensuring only pertinent information is fetched.

To configure prefilters, an individual retriever must first be created and activated within Einstein Studio.

By accessing the Template Settings, admins can navigate to the Search Parameters section and specify prefilter criteria. The prefilter input field facilitates the selection of either static values or dynamic merge fields, which will dictate the data returned. Further refinement of the search can occur at the Search Text field, where keywords can be incorporated along with additional merge fields via the resource picker.

In the Output Fields section, users can adjust the fields being returned, tailoring them according to the desired output.

As a final note, changes to retrievers create new versions, and activation is essential for their incorporation into prompt templates.

Help Link

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Agentforce and Einstein AI + Mulesoft

Mulesoft MCP

MuleSoft now enables the transformation of any application or API into accessible tools and resources for AI agents. This feature leverages the MuleSoft Anypoint Platform to facilitate seamless integration, allowing data from disparate systems to be ingested and utilized by AI agents.

With MCP Support, applications can be registered as MuleSoft connectors, effectively extending the reach of your AI solutions. This allows AI agents to interact with various data sources and triggers, enabling more nuanced and contextual responses during workflows.

By utilizing Visual Builder, developers can create and modify integration flows without extensive coding, thereby accelerating deployment cycles and reducing time-to-value. Hooks into MuleSoft's API-led connectivity approach streamline exposing APIs for AI agents, providing real-time data access and fostering improved decision-making capabilities.

Organizations can utilize AI models and algorithms with real-time transactional data from integrated systems, facilitating advanced analytics and predictive capabilities. Security practices are maintained through MuleSoft’s robust governance features, ensuring that only authorized access is granted to the data being leveraged. This feature supports a broader ecosystem where businesses can enhance customer engagements through intelligent automation and deep understanding of user behavior.

In summary, MuleSoft MCP Support provides a unified framework for embedding AI capabilities into existing business processes, driving operational efficiency and improved user experiences.

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Agentforce Innovations: New variables, filters, and invocable actions enhance agent control and automation. Faster agent creation with templates and Gen AI, plus OTP verification, Agentforce Service Assistant and AI-powered testing.

Data Cloud Enhancements: Agentforce Data Libraries now index knowledge articles and files using grounding techniques, ensuring responses are accurate, personalized, and directly tied to your organization’s data.

Slack Integration: Agentforce integrates directly into Slack, allowing teams to trigger agents via DMs or mentions. Pre-built Slack Actions handle data retrieval, canvas updates, and workflow management, while Slack Enterprise Search enhances contextual responses.

MuleSoft Enhancements: MuleSoft’s API Catalog now syncs directly into Salesforce, simplifying API management. Topic Center streamlines external system integrations, and the new Agentforce and Einstein AI Connectors support low-code AI agent development and secure, trust-layered interactions.

Expanded Language Support: Agentforce Service Agents now support more languages, enabling localized AI interactions across French, German, Italian, Portuguese, Spanish, and more.

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Agentforce

Our product team has introduced a set of powerful enhancements to Agentforce, giving developers and admins advanced tools to integrate, automate, test, and manage AI agents across workflows. These updates enable deeper automation within Flows and Apex, robust agent lifecycle management, and faster delivery of reliable, context-aware AI experiences.

Key Enhancements

1. Control Agent Decision-Making with Variables & Control Access to Topics and Actions with Filters: Enhance Salesforce Agentforce capabilities by leveraging context-driven variables for dynamic agent decision-making, and implement conditional filters to secure and optimize topic and action accessibility.
2. Agents in Flow and APEX and Agent API: Integrate Salesforce Agentforce seamlessly into Flows and Apex using custom invocable actions for automated workflows, and leverage the Agent API for flexible, UI-independent generative AI interactions.
3. Customer Verification, Templates, Agent for Setup, Standard Actions and Progress Indicator: Strengthen security with customizable OTP-based customer verification; accelerate agent creation using specialized templates and Gen AI assistance; streamline admin tasks via Agent for Setup; leverage enhanced standard actions; and improve UX with clear progress indicators.
4. Agentforce Testing Center and AI-Generated Test Cases: Accelerate Salesforce agent testing workflows with batch scenario testing via Agentforce Testing Center, and automate comprehensive test case generation using Gen AI for standard and custom objects.

More information below.

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Enhance Salesforce Agentforce capabilities by leveraging context-driven variables for dynamic agent decision-making, and implement conditional filters to secure and optimize topic and action accessibility.

Control Agent Decision-Making with Variables

Enhance agent autonomy and decision-making by utilizing context, custom conversation, and action output variables within the Salesforce Agent Builder.

• Context Variables: Mapped to object fields, these variables provide read-only access to real-time data during conversations. They're prefixed with $Context and facilitate operations such as maintaining user session context.
• Custom Conversation Variables: Unique to individual agent sessions, these variables store significant data outputs from specific actions, enabling tailored conversational flows. They can be of types: Boolean, Number, or Text.

Practical Usage: Assign a context or custom conversation variable to an action's input for controlled value utilization. Leverage filters to activate topics or actions conditional on variable states. Specify variables in Agent API or topic instructions using formatted references.

To create a custom variable, map an action output to it within the desired topic in the Agent Builder. This capability empowers agents to exhibit refined behavior and adaptability, thereby enhancing the overall customer interaction experience.

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Control Access to Topics and Actions with Filters

You can now implement filters on topics and actions within your agent's framework to restrict access based on predefined conditions. This feature allows you to ensure that agents interact with certain topics or actions only when specified criteria are met, enhancing security and streamlining user interactions.

With the introduction of filters, agent behavior can now incorporate contextual variables. For instance, you can configure a filter to allow access to a Case Management topic solely when a user's case status is marked as Escalated. Similarly, an agent can facilitate a return request only if the user’s order falls within a specific timeframe, such as the last 30 days.

To set up filters, utilize the Context panel in the Agent Builder to define the required variables. Once created, filters can be selectively applied to specific topics or actions, ensuring that the agent retains strict control over functionality based on user authentication or session variables. This capability not only strengthens your conversational agent's efficacy but also meets compliance and security requirements.

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Integrate Salesforce Agentforce seamlessly into Flows and Apex using custom invocable actions for automated workflows, and leverage the Agent API for flexible, UI-independent generative AI interactions.

Agents in Flows & Apex

Custom agent invocable actions enable seamless integration of Agentforce Service Agents or Default Agents into Salesforce flows and Apex classes, facilitating automation of repetitive tasks. By utilizing these actions, users can invoke agents to execute background operations in response to defined triggers.

When creating a custom invocable action, the admin or developer specifies the task through a sample user message, which the agent analyzes to identify the relevant topics and actions. This setup streamlines the task execution process as the agent operates on the context provided from the flow or Apex class. Additionally, context variables can be defined based on the agent's configurations, allowing for the dynamic passing of essential data.

Multiple invocable actions can be included in a single flow to handle diverse tasks or repeated interactions with the same or different agents. This flexible design enhances workflow efficiency and maintains contextual relevance, ensuring that agents can provide timely and appropriate responses to user requests.

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Agent API

The Agent API allows developers to harness generative AI by connecting to Agentforce agents programmatically via a REST API. With this capability, users can establish a seamless interaction with AI agents, facilitating tasks across various platforms and workflows without being constrained by UI limitations.

Key Features

• Headless Agent Deployment: Developers can create and deploy agents that function entirely without a user interface, automating backend processes.
• Standardized Interaction: The API defines standardized endpoints, enabling integration with internal systems and third-party applications effortlessly.
• Session Management: Initiate, send messages to, and end sessions with agents programmatically. This includes the necessary API calls to manage session state and interaction.

This feature empowers organizations to build a robust, agent-based ecosystem, where multiple agents can interact in tandem, invoking one another as necessary, thus optimizing the overall workflow.

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Strengthen security with customizable OTP-based customer verification; accelerate agent creation using specialized templates and Gen AI assistance; streamline admin tasks via Agent for Setup; leverage Agentforce Service Assistant; and improve UX with clear progress indicators.

Implement Customer Verification for Enhanced Security

The Customer Verification topic streamlines secure user interaction for Agentforce Service agents by enforcing authentication before sensitive actions. This feature allows developers to specify which topics and actions require user verification, enhancing adherence to security protocols. When triggered, the agent solicits the user's email to send a one-time passcode (OTP) for access control. Upon entering the correct OTP, the agent retrieves the verified user’s context, enabling access to restricted actions like managing reservations or account details. The process is customizable with filters and variable mappings for tailored security measures.

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Build Specialized Agents with Templates

Agent templates streamline the creation process by providing pre-built configurations tailored to specific business needs, including use-case specific topics, actions, filters, and variables. With this feature, you can create agents such as Agentforce Service Agents or Sales Coaches from existing templates, ensuring consistency and efficiency.

When setting up, users can select an agent template that corresponds to licensed agent types, define the settings, and manage data libraries for context-aware responses. Customization is available post-creation, allowing adjustments in the Agent Builder for optimal functionality.

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Create and Modify Agents and Topics with Gen AI

Want to get started on your first agent (or add to your collection)? Need a little help? Or are you trying to figure out custom topics? Gen AI is here to assist."

Use Gen AI to rapidly develop custom agents and topics. By providing detailed input, Gen AI generates tailored agents based on your requirements. During agent creation, specify the agent's role and company description. You can also add or modify topics directly in the agent setup. Gen AI streamlines agent creation by offering recommendations for improvements, enhancing the overall capabilities of your agent.

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Agent for Setup, Your Sidekick for Admin Tasks

Agent for Setup enhances admin efficiency by integrating AI capabilities into everyday tasks. When enabled, it appears in the Agent Builder, allowing customization with topics, actions, and system messages tailored to specific needs.

Key actions include:

• Answer Questions with Salesforce Documentation
• Get and Explain Object Permissions of User
• Identify Field/Object by Name
• Migrate Connected App

Utilize these actions to troubleshoot and enhance the setup process effectively.

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Agentforce Service Assistant

Agentforce Service Assistant, a Lightning component on the Case record page, enhances case resolution efficiency for service reps. Upon opening a case, it analyzes the feed to generate concise summaries, allowing reps to quickly grasp critical details. With a single click, it provides a tailored, step-by-step service plan based on your organization’s unique data and policies, leveraging AI-grounding techniques. The Assistant utilizes Agentforce topics and instructions to ensure accuracy, improving response times and customer satisfaction without requiring fixed rules or maintenance. Data security is upheld, with no retention of individual case data.

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Agent Progress Indicator

Agentforce (Default) and Agentforce Service agents now feature distinct progress indicators for each action, improving user trust and transparency. Custom actions can display user-defined loading text, enhancing the interaction experience. Previously, agents utilized a set of generic progress indicators, which lacked specificity.

To implement loading text, access the desired action in Agent Builder and enable the Show loading text for this action setting, inputting a custom string in the Loading Text field. Additional configuration is needed for visibility in Messaging for In-App and Web conversations.

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Accelerate Salesforce agent testing workflows with batch scenario testing via Agentforce Testing Center, and automate comprehensive test case generation using Gen AI for standard and custom objects.

Agentforce Testing Center

Agentforce Testing Center facilitates efficient batch testing of agents in the Agent Builder, allowing for the quick assessment of multiple scenarios simultaneously. By utilizing a CSV upload template, you can prepare and run extensive test cases to evaluate topic and action performance.

Upon execution, the results indicate which test cases passed or failed, enabling rapid identification of issues. Failed cases can be retested directly in Agent Builder, offering detailed logs for further troubleshooting.

This feature supports testing in sandbox environments only, as modifications can impact CRM data. Additionally, users should consider request consumption and limits, including supported languages and geo-aware models based on the Salesforce generative AI platform. Overall, the Testing Center enhances the ability to deliver reliable agents efficiently.

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Automated Test Case Generation for Salesforce Objects

Gen AI now enables the creation of automated test cases for standard objects (Account, Lead, Opportunity, and Contact) and custom objects, enhancing the development and QA process. Users can provide specific context in the description to tailor the test case generation. For generating test cases related to custom objects, the description should include the custom object name along with relevant sample utterances, formatted as follows: “Generate test cases for [custom object name]. Sample utterances are: [sample utterance], [sample utterance].”

To create test cases for the Answer Questions with Knowledge action, specify the action, citing relevant knowledge articles: “Generate test cases for the Answer Questions with Knowledge action. Use the following knowledge articles for generating tests: [article title], [article title].” This feature streamlines the testing workflow, ensuring comprehensive coverage and effective validation of Salesforce functionalities.

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Agentforce and Data Cloud

Agentforce Data Library Enhancements

Agentforce Data Library enables improved accuracy and personalization in AI responses by indexing knowledge articles and file uploads using grounding techniques. This feature ensures that AI agents provide responses grounded in your organization's specific information, which enhances the reliability of Large Language Model (LLM) outputs.

Data Libraries streamline configurations across Data Cloud and Prompt Builder by automating tasks such as data streaming and search indexing. The segmentation of knowledge sources into chunks optimizes storage and retrieval efficiency, allowing for faster access to relevant information. The indexing process categorizes these chunks, facilitating quicker and more accurate searches by the AI agents.

The retriever component simplifies data extraction from various databases, minimizing manual intervention. Through standard actions such as Answer Questions with Knowledge, agents efficiently utilize indexed data to respond to user inquiries, ultimately improving the overall user experience.

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Agentforce and Slack

Agentforce now supports direct integration into Slack, facilitating real-time collaboration between employees and digital agents. This integration allows users to engage Agentforce via direct messages or by @ mentioning them in channels, enhancing workflow efficiency without switching platforms. Teams can seamlessly request assistance on tasks such as data retrieval, form completions, and project management directly within their Slack conversations.

The Agentforce Hub serves as a centralized repository, enabling users to search for specific agents based on skill sets, activating relevant agents for a variety of tasks from IT support to marketing strategy.

Additionally, Agentforce now supports pre-built Slack Actions, allowing teams to automate processes such as creating and updating canvases, managing task lists, and constructing workflows. This functionality aims to streamline operations and reduce manual effort in task management. These actions include:

1. Create a Slack Canvas: Facilitates the creation and formatting of a new Slack canvas using provided information.
2. Look Up a Slack User: Enables identification of Slack users by utilizing Salesforce user IDs.
3. Search Slack: Allows for the searching of public Slack messages to retrieve specific information.
4. Send a Slack Direct Message: Enables sending direct messages in Slack, visible in the user-recipient DM.
5. Update a Slack Canvas: Provides the capability to update previously created Slack canvases based on conversation context.

Leveraging Slack Enterprise Search, Agentforce can access and utilize contextual knowledge drawn from conversations and content within Slack. This enables more relevant and precise responses to user queries, tapping into both structured and unstructured data for optimal information delivery. Overall, this integration aims to enhance productivity and facilitate a user-centric approach to digital labor within collaborative environments.

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Agentforce and Einstein AI + Mulesoft

API Catalog

API Catalog provides a unified interface for managing and utilizing APIs within Salesforce, streamlining the automation process. Users can import APIs from the MuleSoft Anypoint Platform by connecting their Salesforce and Anypoint accounts. Once the sync is enabled, API Catalog automatically updates with eligible APIs and their respective versions, allowing users to view detailed specifications and operational statuses.

Users can activate specific API actions for use in Salesforce automations, including flows, Apex code, and agent actions. APIs must conform to specific criteria, such as being RESTful and tagged appropriately in Anypoint Exchange. The catalog also allows for the promotion of API data between Salesforce orgs, ensuring seamless API governance.

Additionally, developers leveraging the Topic Center in Anypoint can enrich their APIs with annotations, enabling better integration with Salesforce agent functionalities. The system supports manual sync restarts and refresh operations to ensure users have the latest data available for automation.

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Mulesoft Topic Center

Topic Center enhances the integration of external applications within Agentforce by defining 'Topics'—curated sets of actions and guidelines for API interactions. MuleSoft Developers can generate these Topics using the Anypoint Code Builder, allowing them to translate use case requirements from Salesforce Admins into actionable API configurations. This streamlined approach enables Topics to manage actions both within Salesforce and in external systems with controlled behavior.

Once developed, APIs are uploaded to Anypoint Exchange, where governance, security, and access controls are applied. The API Catalog within Salesforce provides a centralized repository for all relevant APIs, facilitating easy access for Salesforce Admins as they configure Agentforce capabilities.

Upcoming enhancements to the Topic Center include testing functionalities during the design phase and a monitoring suite for API asset utilization, set for release in late 2025, further solidifying the synergy between MuleSoft and Salesforce in an era of actionable AI.

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Agentforce connector

Agentforce Connector enhances MuleSoft's integration capabilities with Salesforce’s Agentforce platform, enabling developers to build and customize AI agents efficiently. This connector supports low-code development environments, allowing for rapid deployment of AI agents without extensive coding.

Key features include authentication with Salesforce orgs and discovery of active agents, providing a streamlined onboarding experience. Developers can initiate sessions with specified AI agent applications, facilitating real-time interaction.

The connector also enables the amplification of Agentforce's functionalities by allowing external application prompts to be integrated into AI agent workflows. This capability fosters innovative orchestration of customer, partner, and employee interactions, enhancing overall engagement and experience.

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Einstein AI connector

The Einstein AI Connector facilitates integration of Large Language Models (LLMs) with MuleSoft applications through the Salesforce Einstein Trust Layer. With the latest certification by MuleSoft, the open-source connector now follows stringent development and security best practices.

New capabilities include compatibility with Anypoint Code Builder and support for Java 17, enhancing developer experience. The connector can be easily discovered and imported from Anypoint Exchange directly into your Integrated Development Environment (IDE).

Key functionalities of the Einstein AI Connector allow developers to create low-code AI agents and leverage existing investments, such as APIs and templates, for AI agent tooling. All interactions with LLMs are managed securely via the Einstein Trust Layer, while the ability to generate vector embeddings provides essential support for retrieval-augmented generation (RAG) architectures.

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Languages

Agentforce Service Agent in More Languages (Generally Available)

Agentforce Service Agent now supports multiple languages, expanding beyond English and Japanese to include French, German, Italian, Portuguese, and Spanish in certain locales. This is applicable across all enhanced Messaging channels. Language settings can be configured in Agent Builder by specifying a default language and selecting additional options. It is important to note that when operating in a non-English language, system messages will require manual translation. Additionally, the output from some language models may produce variant responses that do not exactly match the designated locale, necessitating careful management of translations and expectations in multilingual interactions.

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Use More Languages with Prompt Builder

In addition to the languages fully supported by the Einstein Trust Layer, you can use other languages supported by Salesforce. However, the Trust Layer features might not be available for the added languages. To use these languages, turn on Global Language Support from Einstein Setup.

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