For the last decade, the role of an Apex developer has been clear: we are the “pro-code” experts. We write the triggers, the complex batch jobs, and the Lightning Web Components that Flow can’t handle. But now, a new word is dominating every conversation: AI.
It’s natural to wonder, “Is Generative AI here to replace my job?”
After spending countless hours digging into the latest Salesforce releases, I’ve come to a clear conclusion: No. AI isn’t replacing the Apex developer. It’s giving our job a massive promotion.
Our role is shifting from being “CRM coders” to becoming “AI Orchestrators.” We are no longer just building the application; we are now the only ones who can teach the AI how to run the business.
In this post, we’ll explore the new, high-value jobs for Apex developers in the age of AI and why our skills are more critical than ever.
First, Let’s Settle the “Clicks vs. Code” Debate
For years, we’ve seen Flow become increasingly powerful. This is a good thing! The “Clicks vs. Code” debate is finally over, and the winner is… everyone.
- Flow is for Automation: It’s the perfect tool for business users and admins to build simple, declarative automations and screen flows. This frees them from long development cycles.
- Apex is for Complexity: This frees us, the developers, to stop working on simple “update a field” requests and focus on high-value, complex problems.
And the biggest, most complex problem to solve right now? Integrating AI.
Understanding the Two Types of AI: The “Assistant” vs. The “Agent”
Before we understand our new role, we need to learn about the two types of AI on the Salesforce platform. They are not the same.
- Einstein Copilot (The AI Assistant): This is your “Iron Man” suit. It’s an AI assistant that helps a human user complete their tasks more efficiently. It sits on the side of the screen and responds to prompts like, “Summarize this case for me” or “Draft an email to this customer.”
- Einstein Agents (The AI Agent): This is your “digital worker.” It is autonomous. An agent can work independently, 24/7, to perform multi-step tasks without human guidance. For example: “A new high-priority case comes in. The agent autonomously checks the customer’s SLA, pings the on-call engineer in Slack, and drafts a ‘we’re on it’ email to the customer.”
Apex developers are the key to unlocking the true power of both. Here’s how.
The 3 New Jobs for Apex in the Age of AI
1. The “Action” Builder (Teaching AI Your Business)
Einstein Copilot is smart, but it doesn’t know your company’s specific business logic. It doesn’t know your unique process for “checking a credit score” or “calculating a custom shipping quote.”
Your new job is to teach it. You do this by creating Custom Copilot Actions.
An “Action” is simply a piece of your Apex code that you make available to the AI. The easiest way? Using the @InvocableMethod annotation. This is the same label you’ve used for years to make your Apex available to Flow, and now it’s your gateway to AI.
When the AI needs to do something complex, it will call your Apex code to get the job done.
Example Code: A Simple “Order Status” Action
Let’s say you want to let the AI answer the question, “What’s the status of order #12345?” You can write a simple Apex class like this:
public class CopilotOrderActions {
// This "InvocableVariable" is the input from the AI
public class OrderRequest {
@InvocableVariable(label=’Order Number’ required=true)
public String orderNumber;
}
// This "InvocableVariable" is the output we send back to the AI
public class OrderStatusResponse {
@InvocableVariable(label=’Order Status’)
public String status;
@InvocableVariable(label=’Estimated Delivery Date’)
public Date estimatedDelivery;
}
/**
* This is the method the AI will call.
* We make it "invocable" so the Einstein 1 Platform can find it.
*/
@InvocableMethod(label=’Get Order Status’
description=’Gets the status and delivery date for a specific order.’)
public static List getOrderStatus(List requests) {
// Get the order number from the request
String orderNumber = requests[0].orderNumber;
// --- Your Business Logic Would Go Here ---
// 1. Query your custom 'Order__c' object
// 2. Find the status and delivery date
// 3. For this example, we'll just return mock data
// --- --- --- --- --- --- --- --- --- --- --
OrderStatusResponse response = new OrderStatusResponse();
response.status = ‘Shipped’;
response.estimatedDelivery = System.today().addDays(3);
return new List{ response };
}
}
By writing this simple class, you’ve just taught the AI how to do its job better. This is the new bread-and-butter of the Apex developer.
2. The “Direct Line” to AI (Calling LLMs from Apex)
What if you want to use Generative AI inside your existing triggers or batch jobs? Now you can.
With the new ModelsAPI class, you can call a Large Language Model (LLM) directly from your Apex code.
This opens up a world of possibilities.
- In a Trigger: When a new Case is created, use Apex to call the AI to automatically analyze its sentiment (is the customer happy, angry, or neutral?) and set the priority.
- In a Batch Job: Run a job over all your Accounts to have the AI generate a “1-paragraph summary” of each account based on its related contacts, opportunities, and cases.
Example Code: Case Sentiment Analysis
Here’s a simplified example of what that Case Trigger logic might look like:
/**
* This is NOT a full, bulk-safe trigger handler.
* It's a simplified example to show the ModelsAPI call.
*/
public class CaseSentimentAnalyzer {
public static void analyzeSentiment(List newCases) {
Case myCase = newCases[0];
// 1. Create the AI prompt
String prompt = ‘Analyze the sentiment of the following customer email. ‘ +
‘Respond with only one word: Positive, Neutral, or Negative.‘ +
‘Email: "’ + myCase.Description + '"’;
// 2. Set up the chat model request
aiplatform.ModelsAPI.ChatModelRequest chatRequest =
new aiplatform.ModelsAPI.ChatModelRequest();
chatRequest.prompt = prompt;
// 3. Call the Einstein model!
// (You would define 'Einstein' in your AI Model settings)
aiplatform.ModelsAPI.ChatModelResponse chatResponse =
aiplatform.ModelsAPI.createChatGenerations(‘Einstein’, chatRequest);
// 4. Get the AI's response
String sentiment = chatResponse.getGenerations()[0].getText().trim();
// 5. Use the response to update your record
myCase.Sentiment__c = sentiment;
// (You would then add this case to a list for DML update)
}
}
This is a superpower. You can now inject AI into any part of your Salesforce process.
3. The “Agent” Orchestrator
This is the big one. As autonomous Einstein Agents become more common, their “brains” will be built from a series of skills. As a developer, you will be the one building the most robust, secure, and complex skills for these agents to use.
While an admin might build a simple agent skill with Flow, you’ll be building the Apex-powered skills that:
- Make complex callouts to external financial systems.
- Process thousands of records from Data Cloud.
- Execute mission-critical logic that must never fail.
The future is a team of human and digital workers, and the digital workers will run on code you write.
Final Thoughts: Your New Role Awaits
AI is not a threat to Apex developers; it’s an opportunity. It’s the force that is finally moving our job from “maintenance” to “innovation.”
Our role is evolving. We are now:
- Teachers, creating custom actions to make the AI smarter.
- Integrators, calling AI models from our code to enhance our apps.
- Architects, building the autonomous agent skills that will power the next generation of business.
The future of Salesforce is complex, connected, and intelligent. And it will be built by developers like us.
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Ravirajsinh Parmar
I am a Salesforce Developer based in Ahmedabad, Gujarat, with over two years of experience building and customizing solutions on the Salesforce platform. I'm passionate about exploring the latest technology trends, especially the intersection of Apex development and AI. I enjoy tackling real-world challenges and sharing what I learn with the Salesforce community.
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