Get Picklist Values Using Dynamic Apex

-

 

The picklist field name and object are the two parameters that the code snippet below requires. We can read either the Picklist label or the value based on these two inputs.


String objectName = 'Account';
String fieldName ='Industry';
  
Schema.SObjectType s = Schema.getGlobalDescribe().get(objectName) ;
Schema.DescribeSObjectResult r = s.getDescribe() ;
Map<String,Schema.SObjectField> fields = r.fields.getMap() ;
Schema.DescribeFieldResult fieldResult = fields.get(fieldName).getDescribe();
List<Schema.PicklistEntry> ple = fieldResult.getPicklistValues();
for( Schema.PicklistEntry pickListVal : ple){
    System.debug(pickListVal.getLabel() +' ---- '+pickListVal.getValue());
}



 

Comments

Post a Comment

Popular posts from this blog

Agentforce in Production: What Separates a Successful Deployment from an AI Demo

-
Image
This perspective comes from my real experience working on Salesforce projects where the challenge was not just building something that worked in a demo, but making sure it could survive real users, real data, real integrations, and real business expectations. In project environments, I have seen how quickly an impressive AI prototype can become difficult to scale if the foundation is not strong. Production success depends on more than prompt quality. It requires trusted data, clear use-case boundaries, secure actions, measurable outcomes, and a practical human handoff model. AI demos are easy to appreciate. A user asks a question. The agent responds naturally. It retrieves information, summarizes records, or performs an action. Everyone in the room sees the potential. Production is different. In production, customers ask incomplete questions. Data is messy. Knowledge articles conflict. APIs fail. Security rules matter. Escalations must work. Costs must be controlled. Business teams exp...
Read more

Introduction to Salesforce Agent Script: Build Predictable AI Agents

-
Image
Please visit here for more details - https://ayaninsights.com/guestblogs/introduction-to-agent-script/ In my latest blog, I introduce Salesforce Agent Script and explain how it helps teams build AI agents that are more predictable, controlled, and aligned with business processes. Large language models are flexible, but relying only on natural-language instructions can sometimes produce inconsistent results. Agent Script addresses this challenge by combining the conversational capabilities of an LLM with structured business logic. The blog explains how developers can use conditions, variables, actions, topics, and transitions to control how an Agentforce agent behaves. Agent Script supports familiar logic such as if/else conditions, value comparisons, sequential actions, and checks for missing data. I also cover its core structure, including system settings, configuration, global variables, languages, connections, and topic blocks. The start_agent topic acts as the entry point for each...
Read more

Salesforce Data Graphs - Best Practices, Limits & Guardrails — 2026 Edition

-
Image
Naming note : Salesforce has rebranded Data Cloud as Data 360. This post uses the new name; everything here applies to orgs still seeing the old label. What a Data Graph really is A Data Graph is a pre-computed, denormalized view of your customer data. You pick a primary Data Model Object (DMO) inside a Data Space, attach related DMOs, calculated insights, and segment memberships, and Data 360 joins all of that into a single read-only JSON blob per primary key. That blob is what gets queried - not the underlying tables - which is why Data Graphs can return sub-second responses even when the source data spans hundreds of millions of rows. High-level architecture: bounded inputs flow into a pre-computed graph, which many consumers read cheaply. There are two flavours, and the difference matters more than most teams realize on day one: • Near real-time Data Graph - the default. Refreshes on a schedule (every 30 minutes on Marketing Cloud data graphs, hourly, every 4 hours, daily, weekly,...
Read more