Mature Capabilities
Method-level generation from comments. Test class scaffolding (often hitting 80%+ coverage on the first pass). Refactoring for null-safe access, bulkification, governor-limit patterns, and WITH USER_MODE migration. ApexDoc and inline comment generation. These work reliably across Vibes 2.0, GitHub Copilot, and Cursor when given good context. Test scaffolding is the highest-ROI use case — generating Test.startTest()/Test.stopTest() blocks, mock data builders, and assertion stubs is mechanical work the model handles well.
// Comment-driven generation that works reliably:
// "bulk-safe trigger handler that updates Account.Rating
// based on Opportunity.Amount sum, respects FLS"
public class AccountRatingHandler extends TriggerHandler {
public override void afterUpdate() {
Set<Id> accountIds = new Set<Id>();
for (Opportunity opp : (List<Opportunity>) Trigger.new) {
accountIds.add(opp.AccountId);
}
Map<Id, AggregateResult> sums = new Map<Id, AggregateResult>(
[SELECT AccountId Id, SUM(Amount) total
FROM Opportunity
WHERE AccountId IN :accountIds
WITH USER_MODE
GROUP BY AccountId]
);
// ... rating update logic
}
}
Less Mature
Complex transaction design with cross-DML coordination. Performance-tuned SOQL with selective index hints. Cross-class refactoring spanning many files. Multi-step architectural changes (e.g., migrating from raw triggers to a framework). AI assists; it does not replace. Review output carefully, especially for governor-limit implications, sharing-mode correctness, and Apex test data setup that bypasses validation rules without comment.
Tool Comparison
Vibes 2.0 understands Salesforce org schema natively (custom objects, fields, sharing model) — advantage for platform-specific work. GitHub Copilot is strong on general code patterns and broader language coverage; useful when you cross from Apex into adjacent languages (Node.js callouts, LWC JS). Cursor offers tight IDE integration with project-wide context and is the strongest at multi-file refactors. Use complementary tools rather than picking one religiously.
Tool Strength Weakness
---- -------- -------
Vibes 2.0 Org schema, FLS-aware, deploy native Lock-in to SF tooling
GitHub Copilot Broad language coverage, fast inline Generic SF patterns
Cursor + Claude Multi-file refactor, project context Schema requires manual sync
JetBrains AI Strong refactoring, weak SF context Less SF-specific tuning
Style Guide Enforcement
Configure AI assistants with your org’s patterns — trigger framework name, selector pattern, naming conventions, FLS enforcement mode, and DML wrapper class. Without configuration, AI defaults to generic Salesforce patterns that may not match your codebase, leading to inconsistent style and review churn. Cursor .cursorrules, Vibes 2.0 workspace settings, and Copilot custom instructions all support this — invest 30 minutes once to save weeks of cleanup later.
.cursorrules excerpt:
- Use TriggerHandler base class for all triggers (not raw triggers)
- Selector pattern: <Object>Selector returning typed lists
- Always include WITH USER_MODE on SOQL unless explicitly system
- Test data via TestDataFactory, never inline
- ApexDoc on all public methods
- No SeeAllData=true in tests
Common Failure Modes
- Accepting generated SOQL without checking selectivity. AI happily writes non-selective queries that pass small-volume tests and fail at scale.
- Skipping the FLS check on generated DML.
WITH USER_MODEshould be the default; AI sometimes omits it. - Over-trusting generated tests. High coverage is not the same as good tests; review assertions, not just line counts.
What to Do This Week
Write a 20-line .cursorrules (or equivalent) that captures your trigger framework, selector pattern, FLS expectation, and test-data convention. Apply it to your assistant of choice and re-run a recent code review.