Category: Salesforce

Master Salesforce data integration and analytics. Tutorials on extracting and handling CRM data using Apex, SOQL, MuleSoft, and the Salesforce Data Cloud for robust enterprise solutions.

  • Your First Salesforce Copilot Action : A 5-Step Guide

    Your First Salesforce Copilot Action : A 5-Step Guide

    The era of AI in CRM is here, and its name is Salesforce Copilot. It’s more than just a chatbot that answers questions; in fact, it’s an intelligent assistant designed to take action. But its true power is unlocked when you teach it to perform custom tasks specific to your business.

    Ultimately, this guide will walk you through the entire process of building your very first custom Salesforce Copilot action. We’ll create a practical tool that allows a user to summarize a complex support Case and post that summary to Chatter with a single command.

    Understanding the Core Concepts of Salesforce Copilot

    First, What is a Copilot Action?

    A Copilot Action is, in essence, a custom skill you give to your Salesforce Copilot. It connects a user’s natural language request to a specific automation built on the Salesforce platform, usually using a Salesforce Flow.

    A flowchart illustrates a User Prompt leading to Salesforce Copilot, which triggers a Copilot Action (Flow). This action seamlessly connects to Apex and Prompt Template, both directing outcomes to the Salesforce Record.

    To see how this works, think of the following sequence:

    1. To begin, a user gives a command like, “Summarize this case for me and share an update.”

    2. Salesforce Copilot then immediately recognizes the user’s intent.

    3. This recognition subsequently triggers the specific Copilot Action you built.

    4. Finally, the Flow connected to that action runs all the necessary logic, such as calling Apex, getting the summary, and posting the result to Chatter.

    Our Project Goal: The Automated Case Summary Action

    Our goal is to build a Salesforce Copilot action that can be triggered from a Case record page. To achieve this, our action will perform three key steps:

    1. It will read the details of the current Case.

    2. Next, the action will use AI to generate a concise summary.

    3. Lastly, it will post that summary to the Case’s Chatter feed for team visibility.

    Let’s begin!

    Building Your Custom Action, Step-by-Step

    Step 1: The Foundation – Create an Invocable Apex Method

    Although you can do a lot in Flow, complex logic is often best handled in Apex. Therefore, we’ll start by creating a simple Apex method that takes a Case ID and returns its Subject and Description, which the Flow can then call.

    The CaseSummarizer Apex Class

    // Apex Class: CaseSummarizer
    public with sharing class CaseSummarizer {
    
        // Invocable Method allows this to be called from a Flow
        @InvocableMethod(label='Get Case Details for Summary' description='Returns the subject and description of a given Case ID.')
        public static List<CaseDetails> getCaseDetails(List<Id> caseIds) {
            
            Id caseId = caseIds[0]; // We only expect one ID
            
            Case thisCase = [SELECT Subject, Description FROM Case WHERE Id = :caseId LIMIT 1];
            
            // Prepare the output for the Flow
            CaseDetails details = new CaseDetails();
            details.caseSubject = thisCase.Subject;
            details.caseDescription = thisCase.Description;
            
            return new List<CaseDetails>{ details };
        }
    
        // A wrapper class to hold the output variables for the Flow
        public class CaseDetails {
            @InvocableVariable(label='Case Subject' description='The subject of the case')
            public String caseSubject;
            
            @InvocableVariable(label='Case Description' description='The description of the case')
            public String caseDescription;
        }
    }
    


    Step 2: The Automation Engine – Build the Salesforce Flow

    After creating the Apex logic, we’ll build an Autolaunched Flow that orchestrates the entire process from start to finish.

    Flow Configuration
    1. Go to Setup > Flows and create a new Autolaunched Flow.
    2. For this purpose, define an input variable: recordId (Text, Available for Input). This, in turn, will receive the Case ID.
    3. Add an Action element: Call the getCaseDetails Apex method we just created, passing the recordId as the caseIds input.
    4. Store the outputs: Store the caseSubject and caseDescription in new variables within the Flow.
    5. Add a “Post to Chatter” Action:
      • Message: This is where we bring in AI. We’ll use a Prompt Template here soon, but for now, you can put placeholder text like {!caseSubject}.
      • Target Name or ID: Set this to {!recordId} to post on the current Case record.
    6. Save and activate the Flow (e.g., as “Post Case Summary to Chatter”).

    Step 3: Teaching the AI with a Prompt Template

    Furthermore, this step tells the LLM how to generate the summary.

    Prompt Builder Setup
    1. Go to Setup > Prompt Builder.
    2. Create a new Prompt Template.
    3. For the prompt, write instructions for the AI. Specifically, use merge fields to bring in your Flow variables.
    You are a helpful support team assistant.
    Based on the following Case details, write a concise, bulleted summary to be shared with the internal team on Chatter.
    
    Case Subject: {!caseSubject}
    Case Description: {!caseDescription}
    
    Summary:
    

    4. Save the prompt (e.g., “Case Summary Prompt”).

    Step 4: Connecting Everything with a Copilot Action

    Now, this is the crucial step where we tie everything together.

    Action Creation
    1. Go to Setup > Copilot Actions.
    2. Click New Action.
    3. Select Salesforce Flow as the action type and choose the Flow you created (“Post Case Summary to Chatter”).
    4. Instead of using a plain text value for the “Message” in your Post to Chatter action, select your “Case Summary Prompt” template.
    5. Follow the prompts to define the language and behavior. For instance, for the prompt, you can use something like: “Summarize the current case and post it to Chatter.”
    6. Activate the Action.

    Step 5: Putting Your Copilot Action to the Test

    Finally, navigate to any Case record. Open the Salesforce Copilot panel and type your command: “Summarize this case for me.”

    Once you issue the command, the magic happens. Specifically, the Copilot will understand your intent, trigger the action, run the Flow, call the Apex, generate the summary using the Prompt Template, and post the final result directly to the Chatter feed on that Case.

    Conclusion: The Future of CRM is Action-Oriented

    In conclusion, you have successfully built a custom skill for your Salesforce Copilot. This represents a monumental shift from passive data entry to proactive, AI-driven automation. Indeed, by combining the power of Flow, Apex, and the Prompt Builder, you can create sophisticated agents that understand your business and work alongside your team to drive incredible efficiency.

  • Salesforce Agentforce: Complete 2025 Guide & Examples

    Salesforce Agentforce: Complete 2025 Guide & Examples

    Autonomous AI Agents That Transform Customer Engagement

    Salesforce Agentforce represents the most significant CRM innovation of 2025, marking the shift from generative AI to truly autonomous agents. Unveiled at Dreamforce 2024, Salesforce Agentforce enables businesses to deploy AI agents that work independently, handling customer inquiries, resolving support tickets, and qualifying leads without human intervention. This comprehensive guide explores how enterprises leverage these intelligent agents to revolutionize customer relationships and operational efficiency.

    Traditional chatbots require constant supervision and predefined scripts. Salesforce Agentforce changes everything by introducing agents that reason, plan, and execute tasks autonomously across your entire CRM ecosystem.


    What Is Salesforce Agentforce?

    Salesforce Agentforce is an advanced AI platform that creates autonomous agents capable of performing complex business tasks across sales, service, marketing, and commerce. Unlike traditional automation tools, these agents understand context, make decisions, and take actions based on your company’s data and business rules.

    A split image compares an overwhelmed man at a laptop labeled Old Way with a smiling robot surrounded by digital icons labeled AgentForce 24/7, illustrating traditional vs. automated customer service.

    Core Capabilities

    The platform enables agents to:

    • Resolve customer inquiries autonomously across multiple channels
    • Qualify and prioritize leads using predictive analytics
    • Generate personalized responses based on customer history
    • Execute multi-step workflows without human intervention
    • Learn from interactions to improve performance over time

    Real-world impact: Companies using Salesforce Agentforce report 58% success rates on simple tasks and 35% on complex multi-step processes, significantly reducing response times and operational costs.


    Key Features of Agentforce AI

    xGen Sales Model

    The xGen Sales AI model enhances predictive analytics for sales teams. It accurately forecasts revenue, prioritizes high-value leads, and provides intelligent recommendations that help close deals faster. Sales representatives receive real-time guidance on which prospects to contact and what messaging will resonate.

    Two robots are illustrated. The left robot represents xGEN SALES with a rising bar graph and user icons. The right robot represents XLAM SERVICE with a 24/7 clock and documents, symbolizing round-the-clock support.

    xLAM Service Model

    Designed for complex service workflows, xLAM automates ticket resolution, manages customer inquiries, and predicts service disruptions before they escalate. The model analyzes historical patterns to prevent issues proactively rather than reactively addressing complaints.

    Agent Builder

    The low-code Agent Builder empowers business users to create custom agents without extensive technical knowledge. Using natural language descriptions, teams can define agent behaviors, set guardrails, and deploy solutions in days rather than months.

    A diagram showing four steps in a process: Define Purpose, Set Rules, Add Guardrails, and a hand icon clicking Deploy Agent. The steps are shown in a stylized web browser window.

    How Agentforce Works with Data Cloud

    Salesforce Agentforce leverages Data Cloud to access unified customer data across all touchpoints. This integration is critical because AI agents need comprehensive context to make informed decisions.

    Unified Data Access

    Agents retrieve information from:

    • Customer relationship history
    • Purchase patterns and preferences
    • Support interaction logs
    • Marketing engagement metrics
    • Real-time behavioral data

    Retrieval Augmented Generation (RAG)

    The platform uses RAG technology to extract relevant information from multiple internal systems. This ensures agents provide accurate, contextual responses grounded in your organization’s actual data rather than generic outputs.

    Why this matters: 80% of enterprise data is unstructured. Data Cloud harmonizes this information, making it accessible to autonomous agents for better decision-making.


    Real-World Use Cases

    Use Case 1: Autonomous Customer Service

    E-commerce companies deploy service agents that handle common inquiries 24/7. When customers ask about order status, return policies, or product recommendations, agents provide instant, accurate responses by accessing order management systems and customer profiles.

    Business impact: Reduces support ticket volume by 40-60% while maintaining customer satisfaction scores.

    Use Case 2: Intelligent Lead Qualification

    Sales agents automatically engage with website visitors, qualify leads based on predefined criteria, and route high-value prospects to human representatives. The agent asks qualifying questions, scores responses, and updates CRM records in real-time.

    Business impact: Sales teams focus on ready-to-buy prospects, increasing conversion rates by 25-35%.

    Use Case 3: Proactive Service Management

    Service agents monitor system health metrics and customer usage patterns. When potential issues are detected, agents automatically create support tickets, notify relevant teams, and even initiate preventive maintenance workflows.

    Business impact: Prevents service disruptions, improving customer retention and reducing emergency support costs.


    Getting Started with Agentforce

    Step 1: Define Your Use Case

    Start with a specific, high-volume process that’s currently manual. Common starting points include:

    • Customer inquiry responses
    • Lead qualification workflows
    • Order status updates
    • Appointment scheduling

    Step 2: Prepare Your Data

    Ensure Data Cloud has access to relevant information sources:

    • CRM data (accounts, contacts, opportunities)
    • Service Cloud data (cases, knowledge articles)
    • Commerce Cloud data (orders, products, inventory)
    • External systems (ERP, marketing automation)

    Step 3: Build and Train Your Agent

    Use Agent Builder to:

    1. Describe agent purpose and scope
    2. Define decision-making rules
    3. Set guardrails and escalation paths
    4. Test with sample scenarios
    5. Deploy to production with monitoring

    Step 4: Monitor and Optimize

    Track agent performance using built-in analytics:

    • Task completion rates
    • Customer satisfaction scores
    • Escalation frequency
    • Resolution time metrics

    Continuously refine agent instructions based on performance data and user feedback.


    Best Practices for Implementation

    Start Small and Scale

    Begin with a single, well-defined use case. Prove value before expanding to additional processes. This approach builds organizational confidence and allows teams to learn agent management incrementally.

    Establish Clear Guardrails

    Define when agents should escalate to humans:

    • Complex negotiations requiring judgment
    • Sensitive customer situations
    • Requests outside defined scope
    • Regulatory compliance scenarios

    Maintain Human Oversight

    While agents work autonomously, human supervision remains important during early deployments. Review agent decisions, refine instructions, and ensure quality standards are maintained.

    Invest in Data Quality

    Agent performance depends directly on data accuracy and completeness. Prioritize data cleansing, deduplication, and enrichment initiatives before deploying autonomous agents.


    Pricing and Licensing

    Salesforce Agentforce pricing follows a conversation-based model:

    • Charged per customer interaction
    • Volume discounts available
    • Enterprise and unlimited editions include base conversations
    • Additional conversation packs can be purchased

    Organizations should evaluate expected interaction volumes and compare costs against manual handling expenses to calculate ROI.


    Integration with Existing Salesforce Tools

    Einstein AI Integration

    Agentforce builds on Einstein AI capabilities, leveraging existing predictive models and analytics. Organizations with Einstein implementations can extend those investments into autonomous agent scenarios.

    Slack Integration

    Agents operate within Slack channels, enabling teams to monitor agent activities, intervene when necessary, and maintain visibility into customer interactions directly in collaboration tools.

    MuleSoft Connectivity

    For enterprises with complex system landscapes, MuleSoft provides pre-built connectors that allow agents to interact with external applications, databases, and legacy systems seamlessly.


    Future of Autonomous Agents

    Multi-Agent Collaboration

    The 2025 roadmap includes enhanced multi-agent orchestration where specialized agents collaborate on complex tasks. For example, a sales agent might work with a finance agent to create custom pricing proposals automatically.

    Industry-Specific Agents

    Salesforce is developing pre-configured agents for specific industries:

    • Financial Services: Compliance checking and risk assessment
    • Healthcare: Patient engagement and appointment optimization
    • Retail: Inventory management and personalized shopping assistance
    • Manufacturing: Supply chain coordination and quality control

    Continuous Learning Capabilities

    Future releases will enable agents to learn from every interaction, automatically improving responses and decision-making without manual retraining.


    Common Challenges and Solutions

    Challenge 1: Trust and Adoption

    Solution: Start with low-risk use cases, maintain transparency about agent involvement, and demonstrate value through metrics before expanding scope.

    Challenge 2: Data Silos

    Solution: Implement Data Cloud to unify information across systems, ensuring agents have comprehensive context for decision-making.

    Challenge 3: Over-Automation

    Solution: Maintain balanced automation by defining clear escalation paths and preserving human touchpoints for high-value or sensitive interactions.


    Conclusion: Embracing Autonomous AI

    Salesforce Agentforce represents a fundamental shift in how businesses automate customer engagement. By moving beyond simple chatbots to truly autonomous agents, organizations can scale personalized service while reducing operational costs and improving customer satisfaction.

    Success requires thoughtful implementation—starting with well-defined use cases, ensuring data quality, and maintaining appropriate human oversight. Companies that adopt this technology strategically will gain significant competitive advantages in efficiency, responsiveness, and customer experience.

    The future of CRM automation is autonomous, intelligent, and available now through Salesforce Agentforce. Organizations ready to embrace this transformation should begin planning their agent strategy today.


    External Resources