Shipping AI-Generated Code: What Happens After "It Works"

The Pre-Deployment Checklist ## Does it handle real edge cases? ## Does it follow your org's patterns? ## Is the documentation sufficient? ## Deploying with Confidence ## When Requirements Change ## Modifying manually ## Regenerating completely ## Incremental AI updates ## Which approach should you choose? ## Avoiding Technical Debt ## Deprecated patterns are insidious ## Pattern consistency compounds over time ## Documentation degrades faster ## Measuring Success ## The Bigger Picture ## Where to Go From Here ## Series Recap Part 4 of 4: Agentforce Vibes Series The moment your AI-generated component passes tests and works in your sandbox, a deceptively simple question appears: Is it ready to deploy? In this series so far we have built a working Lightning Web Component with Agentforce Vibes, and developed a systematic way to review the AI-generated code. Those steps get you to the point where "it works." But shipping to production and maintaining code over months or years requires thinking beyond initial functionality. This article covers the final piece: how to confidently deploy AI-generated code, iterate on it when requirements change, and avoid creating technical debt that haunts you later. Before deploying any code to production, experienced developers run through a mental checklist. With AI-generated code, that checklist expands slightly because you're validating both correctness and the AI's interpretation of your requirements. AI training data tends to over-represent happy paths because that's what most code examples demonstrate. Your production environment contains messier reality: null values in unexpected places, users with partial permissions, records in locked states, concurrent updates, and combinations of data that seemed impossible when you wrote the requirements. Test specifically for edge cases the AI might have missed: If your test coverage doesn't include these scenarios, you will be discovering them in production. This matters more than it seems initially. AI-generated code might work correctly but use different patterns than your existing codebase. One trigger might use a framework while another handles everything inline. One component might structure error handling differently than similar components. These inconsistencies create maintenance burden. Developers looking at your codebase six months from now need to recognize patterns. "This looks like our other contact search components" is much better than "Why is this one completely different?" Review the generated code against your team's style guides, architectural patterns, and existing implementations. If you have a trigger framework, the AI-generated trigger should use it. If your components follow specific naming conventions for methods and variables, the generated code should match. AI-generated code often lacks comments explaining why choices were made because the AI doesn't know your business context. It can document what the code does, but not why it exists or what business rules it implements. Add comments explaining business logic, edge case handling, and architectural decisions. Future developers—including yourself—will need context that isn't obvious from the code alone. "Why does this field validation work this way?" shouldn't require git archaeology to answer. Once you've verified these aspects, deployment follows your normal process: change sets, SFDX deployments, or whatever your org uses. The code itself isn't special just because AI generated it. What is different is your deployment strategy. For your first few AI-generated components, consider more conservative rollouts than you might use for hand-written code: Deploy to a partial release org first if you have one. Let it handle production traffic for a day or two while you monitor for issues. This catches problems that don't appear in sandbox testing but emerge with real user behavior. Enable debug logging initially even if you normally wouldn't. The first time an AI-generated component runs in production, you want visibility into what it's actually doing. You can reduce logging once you're confident it's working correctly. Monitor error rates actively for the first week. Set up alerts for exceptions, failed transactions, or unexpected behavior. Don't just deploy and assume silence means success. Have a rollback plan that's actually tested. Can you quickly disable the component if something goes wrong? This is good practice for any deployment, but especially important when you're less certain about implementation details. Here's where things get interesting. Three months after deploying your contact search component, someone asks: "Can we also search by account name, not just contact name?" You have three options: Each approach has trade-offs. This is what you'd do with human-written code. Read the existing implementation, understand how it works, add the new feature following the same patterns. Pros: You maintain full control. You understand exactly what changed. The modification integrates cleanly with existing code. Cons: You're now maintaining code you didn't write. If the original implementation was complex or used patterns you wouldn't have chosen, maintenance becomes harder. When to use this: For small, well-understood changes. Adding a field to a query, adjusting validation logic, tweaking UI layouts. Changes where you can see exactly what needs to happen. Ask Agentforce Vibes to rebuild the component from scratch with updated requirements. This gets you fresh code that might incorporate better patterns or newer platform features than the original. Pros: You get a clean implementation that considers all requirements together, not just bolted-on features. The AI might generate better code now than it did originally. Cons: You lose any manual refinements you made to the original code. Tests need updating. Users might notice UI changes even if functionality is similar. Redeployment carries all the usual risks. When to use this: For significant changes that affect core architecture. When the original code has become messy through multiple modifications. When you want to take advantage of newer AI capabilities or platform features. Give the AI your existing code and ask it to add the specific new feature. This is like pair programming: "Here's what we have, here's what I need, update it accordingly." Pros: Preserves existing functionality and manual refinements. Changes are targeted and reviewable. Less deployment risk than full regeneration. Cons: The AI might not maintain the original code's patterns consistently. You need to review how the new feature integrates with existing code. Over time, the component becomes a patchwork of different AI interactions. When to use this: For moderate changes to stable code. When you've invested significant manual refinement effort and don't want to lose it. When the change is additive rather than transformational. There's no universal answer—it depends on the scope of change and the state of your existing code. In practice, I follow a simple decision tree: The key insight: with systematic review and adherence to your org's patterns, AI-generated code should be indistinguishable from manually written code in quality and maintainability. The danger isn't AI generation itself—it's accepting inconsistent patterns across multiple AI interactions, which gradually turns your codebase into spaghetti. Each modification, whether manual or AI-assisted, should maintain or improve architectural coherence, not erode it. The biggest risk with AI-generated code isn't that it won't work initially—it's that it creates technical debt you don't notice until maintenance becomes painful. Technical debt with AI code looks different than traditional technical debt. It's not necessarily messy code or poor architecture. It's code that works but doesn't match your team's patterns, uses deprecated approaches that the AI learned from older training data, or solves problems in ways your team wouldn't choose. AI models train on code written over many years. Salesforce's platform evolves constantly. The AI might generate code using @track decorators in Lightning Web Components—which still work but are unnecessary in modern LWC and signal outdated patterns to experienced developers. This isn't wrong exactly, but it's not current either. Six months from now when Salesforce deprecates that pattern, you have technical debt to address. A year from now when new developers join your team, they'll wonder why some components use outdated approaches. Review generated code specifically for platform currency. Does it use current best practices? Would this code appear in Salesforce's latest documentation and examples? If not, refactor to current patterns even if the original code works. Your first AI-generated component might use slightly different patterns than your existing code. That's one inconsistency in your codebase. Your tenth AI-generated component might use patterns different from your existing code and the first nine AI components. Now you have ten different approaches to similar problems. This compounds quickly. Establish clear patterns for common operations—error handling, user input validation, data queries, UI feedback—and verify AI-generated code follows them. When it doesn't, refactor immediately rather than accepting the technical debt. AI-generated code often includes basic documentation but not business context. "This method searches contacts" is accurate but not useful six months later when someone asks "Why does it filter out contacts with no email?" Immediate documentation pays dividends. Capture the original prompt, the business requirements, edge cases you explicitly tested, and decisions you made during review. Future modifications need this context. I maintain a simple documentation template for AI-generated components: This takes five minutes to write but saves hours during future modifications. How do you know if your AI-generated code is actually successful beyond "it works"? Traditional metrics still apply: Does it solve the business problem? Do users find it useful? Does it perform acceptably? Is it reliable? But there are AI-specific success indicators worth tracking: Time to delivery: Did using AI actually ship faster than hand-coding would have? Be honest about total time including review, refinement, and deployment preparation, not just initial generation. Maintenance burden: Is this code easier or harder to maintain than similar hand-written components? Track how long modifications take and how often they introduce bugs. Knowledge transfer: Can other developers understand and modify this code? If only the original developer can maintain it because they remember the AI interactions, that's a warning sign. Technical debt velocity: Is AI-generated code creating technical debt faster than you can address it? Are you constantly refactoring AI code to match current patterns? The AI doesn't determine long-term success. Your review process and maintenance discipline do. This series started with a simple question: Can you really build production Salesforce features by describing them to an AI? The answer is yes, but with important caveats. AI code generation is legitimate development, not a shortcut around proper engineering. The prompts replace some typing but not thinking. Review replaces some coding but not expertise. The AI handles implementation details you specify clearly; you handle everything you didn't specify, which is often where real complexity lives. Used well, AI generation accelerates the mechanical parts of development—converting requirements into code, implementing standard patterns, generating boilerplate—while freeing you to focus on architecture, business logic, edge cases, and integration. You ship faster not because the AI is magic but because you can focus attention on decisions that actually require human judgment. Used poorly, AI generation creates code that works initially but becomes maintenance burden—technically correct but architecturally inconsistent, functionally adequate but poorly documented, passing tests but hiding edge cases. The difference isn't the AI. It's how deliberately you approach prompting, review, deployment, and maintenance. If you're starting to use Agentforce Vibes or similar tools: Start small. Generate components you understand well enough to review thoroughly. Build confidence in your review process before tackling complex requirements. Develop checklists. Systematic review catches more issues than ad-hoc inspection. Start with the functional and security checks from Part 3, then expand based on issues you discover. Document patterns. As you refine AI-generated code, capture the patterns that work for your org. These become templates for future prompts and review guidelines. Track what works. Pay attention to which prompts produce better code, which review steps catch the most issues, which modifications you make repeatedly. This feedback loop improves your AI development process faster than any article can. Stay current. AI models improve constantly. Salesforce's platform evolves regularly. The best practices for AI-generated code today will need refinement as both advance. AI code generation isn't replacing software development. It's changing what parts of development require human attention and what parts can be automated. That's a significant shift, but it still requires developers who understand what good code looks like, how to verify it works correctly, and how to maintain it over time. The AI handles more implementation. You handle everything else. And "everything else" remains substantial, nuanced, and irreducibly human. Tags: #salesforce #ai #agentforce #webdev #coding #softwaredevelopment #programming Templates let you quickly answer FAQs or store snippets for re-use. Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink. Hide child comments as well For further actions, you may consider blocking this person and/or reporting abuse - Required fields that are somehow null (legacy data, integration bugs) - Users who lack permissions you assumed they'd have - Empty search results, empty lists, empty strings versus null - Very long strings that break UI layouts - Special characters in user input - Concurrent modifications to the same records - Modify the AI-generated code manually - Ask the AI to regenerate the entire component with updated requirements - Ask the AI to add just the new feature to existing code - Original prompt: What I asked the AI to build - Business requirements: What problem this solves - Key decisions: Choices I made during review or refinement - Edge cases tested: Scenarios I explicitly verified - Known limitations: What this component doesn't handle - Part 1: What Is Agentforce Vibes? - Part 2: From Prompt to UI: Building Your First Component - Part 3: Do You Need to Understand AI-Generated Salesforce Code? - Part 4: Shipping AI-Generated Code: What Happens After "It Works" (you are here)