Development Studio
AI Salesforce development with a real feedback loop
Author and deploy Apex classes, Lightning Web Components, Flows, permission sets, objects, and layouts in a familiar editor, with an AI pair programmer that reads the actual deployment result and iterates until it compiles.
Move Cases closed more than two years ago into Case_Archive__c and delete the originals — 3 components pulled from DevSandbox. Edits are saved here, not in Salesforce, until you deploy.
| 1 | /** Archives Cases closed more than two years ago, 200 at a time. */ |
| 2 | global with sharing class ArchiveClosedCasesBatch implements Database.Batchable<SObject> { |
| 3 | |
| 4 | private static final Integer RETENTION_DAYS = 730; // two years |
| 5 | |
| 6 | global Database.QueryLocator start(Database.BatchableContext ctx) { |
| 7 | Date cutoff = Date.today().addDays(-RETENTION_DAYS); |
| 8 | return Database.getQueryLocator( |
| 9 | 'SELECT Id, CaseNumber, ClosedDate FROM Case ' |
| 10 | + 'WHERE IsClosed = true AND ClosedDate < :cutoff ' |
| 11 | + 'ORDER BY ClosedDate ASC' |
| 12 | ); |
| 13 | } |
| 14 | |
| 15 | global void execute(Database.BatchableContext ctx, List<Case> scope) { |
| 16 | List<Case_Archive__c> rows = new List<Case_Archive__c>(); |
| 17 | for (Case c : scope) { |
| 18 | rows.add(new Case_Archive__c(Case__c = c.Id, Closed_On__c = c.ClosedDate)); |
| 19 | } |
| 20 | insert rows; |
| 21 | } |
| 22 | } |
What you can do
- VS Code-style editor for Apex, LWC, Flow XML, permission sets, custom objects, fields, validation rules, global value sets, and page layouts
- One-click apply: AI-generated code drops straight into the editor
- Deploy through the Metadata API with configurable test levels (RunLocalTests, RunSpecifiedTests, and more)
- Validate against production first, then quick-deploy that passed validation without re-running tests
- The AI reads deployment failures (compile errors with line numbers, Apex test failures) and fixes the actual problem
- Instant same-org save for Apex and LWC in a sandbox, the Developer Console flow without the Developer Console
- Your live org as context while coding: schema, existing classes, automation, live queries
- Built-in playbooks for Apex patterns, LWC structure, flow design, and permission sets
- Lightning pages: pull a record, app or home page into a project, edit its FlexiPage, and deploy it, or ask the AI what is on a page first
- Track every project by stage (Planned to Done) and whether it has reached production, set automatically when a production deploy succeeds
How it works
Describe what to build
“A trigger handler that syncs Opportunity amounts to a rollup on Account”. The AI grounds itself in your real schema first.
Review in the editor
Generated files land in a familiar editor. Change anything before it goes near your org.
Deploy with tests
Ship through the Metadata API at the test level you choose. Production needs an explicit confirmation, and you can validate first.
Let the AI fix failures
Compile errors and test failures flow back into the conversation with line numbers. The AI iterates on evidence, not guesses.
Why it's different
It reads the real errors
Most AI coding tools stop at “here’s some Apex, good luck.” The Studio fetches the deployment result (component errors, failing assertions, line numbers) and feeds it back to the AI, which then fixes the actual problem rather than guessing at a new version. Writes need the “Deploy & write” permission and are off entirely unless your deployment enables them, and a production target requires an explicit per-run confirmation.
Try asking
- “Write a batchable Apex job that archives closed cases older than two years.”
- “Build an LWC datatable of open opportunities with inline stage editing.”
- “Create a permission set for the finance team: read on Invoices__c, no delete.”
- “Fix this deployment — two test classes are failing.”
Development Studio — questions
Works even better with
Deployments
Diff two orgs. Pick what ships.
Learn moreTeam Conflicts
Know when a teammate is changing the same component, in any sandbox, before a deploy overwrites their work.
Learn moreMCP Server
Point ChatGPT, Claude, Claude Code or Codex at your org. Same tools, same guardrails, your own AI subscription.
Learn moreReplacing something?
- orgadmin.ai vs Agentforce
Salesforce’s platform for customer-facing AI agents. Compare when what you wanted was AI for the people who run the org.
Read next
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- Agentforce, Einstein, or your own AI key?
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- Why Salesforce deployments fail — and how to read the errors
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- Developer Console not visible in Salesforce? Why it went missing and how to get it back
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- How to onboard junior Salesforce admins and developers faster
Juniors are slow to ramp up because the org is undocumented, practice is risky and feedback is slow. Here is a 30-60-90 day plan that fixes all three, and where orgadmin.ai helps.
- Overwrote a teammate's Salesforce changes? How to stop deployments undoing each other's work
Salesforce has no merge and no lock for metadata, so the last deployment wins and a colleague's work can vanish without an error. Here is why it happens, the habits that prevent it, and how orgadmin.ai's team conflict detection flags overlapping work across sandboxes before anyone deploys.
Put Development Studio to work on your org
Every AI feature on your own provider key, from A$14.99 per user per month.
- 14-day free trial
- No credit card required
- Cancel any time