Part 3 of 4: Agentforce Vibes Series
The debate started in a Slack channel I follow for Salesforce developers. Someone had asked Agentforce Vibes to build a trigger for updating account records, reviewed the generated code briefly, saw that tests passed, and deployed it to production. Another developer replied: "You deployed code you don't fully understand? That's dangerous." The original poster pushed back: "If the tests pass and it works, why does it matter whether I understand every line?"
This isn't a theoretical question anymore. As AI code generation becomes standard practice, every developer using these tools will face this moment: Do I need to understand this code, or can I trust the AI to have generated it correctly?
The honest answer is more nuanced than either extreme. You don't need to understand every implementation detail the way you would if you'd written it from scratch. But you absolutely need to understand enough to know whether the code is doing what you think it's doing, and whether it will continue working in the ways that matter.
The Two Schools of Thought
The conversation in that Slack channel reflects a genuine split in how developers approach AI-generated code. On one side are developers who argue that understanding is non-negotiable. Code you don't understand is code you can't maintain, debug, or trust. If you wouldn't deploy human-written code without reviewing it carefully, why would AI-generated code deserve less scrutiny?
On the other side are developers who see AI as fundamentally changing what "understanding" means. Modern development already involves using libraries, frameworks, and platform features whose implementations we don't fully understand. We trust React's reconciliation algorithm without studying its source code. We use Salesforce's Lightning Data Service without implementing our own caching layer. Why should AI-generated code be different? If it passes tests and works correctly, the implementation details may not matter.
Both perspectives contain truth, but both miss something important. The question isn't whether to understand AI-generated code in general—it's what aspects of it you need to understand, and how deeply, to deploy it responsibly.
What You Actually Need to Understand
Let me return to that trigger example, because it illustrates the levels of understanding that matter. The developer who deployed it without deep review likely understood the business logic: when an account changes, update related records in a specific way. Tests confirmed this behavior worked. But there are other dimensions of understanding that matter for production code.
Do you understand the performance characteristics? Triggers in Salesforce operate in bulk context—a single operation might process 200 records. Does the generated trigger handle bulk operations correctly, or does it query inside loops and hit governor limits under real-world load? Tests might pass with one or two records but fail spectacularly with bulk data imports.
Do you understand the security model? The trigger needs to respect field-level security and sharing rules. Did the AI generate code that enforces these properly, or does it bypass security checks in ways that expose sensitive data? This isn't about implementation details—it's about whether the code creates security vulnerabilities.
Do you understand the edge cases? What happens if required fields are null? If related records don't exist? If the account is in a locked state? AI-generated code often handles the happy path correctly while missing edge cases that only become apparent through careful review or production incidents.
Do you understand the architectural fit? Does this trigger follow your org's patterns for trigger handling—using a trigger framework if you have one, or creating technical debt if it uses a different pattern than your existing triggers? Will it conflict with other automation? These questions aren't about the trigger's internal implementation, but about how it fits into your existing codebase.
The distinction matters. You don't need to understand why the AI chose a particular variable name or whether it could have used a slightly more elegant algorithm. You do need to understand whether the code is secure, performant, architecturally appropriate, and handles edge cases correctly. These are different types of understanding, requiring different review approaches.
Building Systematic Review Habits
If you're going to use AI to generate code, you need systematic ways to assess these dimensions quickly. Ad-hoc review doesn't work—it's too easy to assume the AI got things right or to spend time on irrelevant details while missing critical issues.
Start with the question: "What could go wrong with this code in production?" For Salesforce development, this usually means checking a specific set of concerns. Is it bulkified correctly? Does it enforce security properly? Does it handle governor limits? Does it follow org patterns? Does it handle errors gracefully? These aren't implementation details—they're quality gates.
Normally I run through the following checklist when reviewing AI-generated Apex code. It takes about five minutes and catches the majority of issues that would cause production problems:
Bulkification: Does the code process collections properly, or does it query/DML inside loops? Run it mentally with 200 records—does it hit governor limits?
Security: Does it run with proper sharing? Does it check field-level security? Could users access data they shouldn't through this code?
Governor limits: Beyond bulkification, are there other limit risks? Total SOQL queries, DML statements, CPU time for complex operations?
Error handling: What happens when something goes wrong? Are exceptions caught appropriately? Will users see helpful error messages or mysterious failures?
Edge cases: What if data is missing, malformed, or unexpected? Does the code fail gracefully or crash?
Architectural fit: Does this follow our trigger framework? Does it match how we handle similar operations? Will it conflict with existing automation?
This isn't a comprehensive code review—it's a focused assessment of production-readiness. The AI might have implemented the business logic in ways I wouldn't have chosen, but that often doesn't matter. What matters is whether it created any of these common failure modes.
For Lightning Web Components, the checklist is different but equally systematic. Does it follow current LWC patterns or use deprecated approaches? Does it handle loading and error states? Does it implement proper debouncing for user interactions? Does it follow Lightning Design System conventions? Again, this takes minutes, not hours, because you're checking specific concerns rather than reviewing every line.
The Trust-Building Process
After using Agentforce Vibes I have changed my review process in a specific way. I'm not reviewing less carefully—I'm reviewing more efficiently because I know what the AI tends to get right and where it tends to create problems.
Early on, I reviewed everything in detail, because I had no intuition about AI-generated code quality. I found patterns. The AI is remarkably good at basic CRUD operations and straightforward business logic. It's less reliable at performance optimization, security enforcement, and handling edge cases. It almost always generates syntactically correct code but frequently uses outdated patterns that work but aren't current.
This pattern recognition lets me focus review effort where it matters. When the AI generates a simple query and display component, I can quickly verify it's secure and performant without studying every implementation choice. When it generates complex trigger logic with conditional updates, I review much more carefully because that's where subtle bugs hide.
This is similar to how you might review code from different team members. You trust experienced developers to handle certain tasks with light review, while providing more detailed feedback to junior developers. It's not about blind trust—it's about calibrating review intensity to risk.
The critical difference with AI is that you're building a mental model of its capabilities and limitations rather than a specific developer's skills. The AI doesn't learn from your feedback the way a person would. But you learn which types of code generation require more scrutiny and which concerns are most likely to be problematic.
When Understanding Matters Most
There are situations where you need deeper understanding of AI-generated code, even if it appears to work correctly. These aren't arbitrary—they're contexts where the cost of problems is high or where shallow understanding creates specific risks.
Code that handles sensitive data or enforces security requires deeper understanding. You need to verify not just that security checks exist, but that they're implemented correctly and cover all access paths. A security vulnerability in generated code is just as serious as one you write yourself, and "the AI generated it" isn't an acceptable excuse.
Code that integrates with external systems deserves careful review. API integrations, callouts to third-party services, and data synchronization logic often have subtle requirements that aren't obvious from tests. Understanding how the integration works helps you anticipate what might break when external systems change or behave unexpectedly.
Code that will be maintained by others should be comprehensible to your team, not just to you. If the AI generated something that works but is confusing, that creates maintenance problems. Sometimes it's worth refactoring AI-generated code not because it's wrong, but because your team won't be able to maintain it effectively.
Complex business logic often requires deeper understanding, particularly when requirements are likely to change. If you don't understand how the AI implemented a complex calculation or workflow, you'll struggle to modify it when requirements evolve. For straightforward logic, shallow understanding is fine. For complex rules engines or intricate workflows, you need to understand the implementation approach.
Performance-critical code deserves extra scrutiny. If a component needs to handle large data volumes or respond quickly under load, you need to understand its performance characteristics. The AI might generate functionally correct code that performs poorly at scale, and you won't catch this without understanding how it works.
The Accountability Question
Here's the uncomfortable truth about using AI-generated code: when it fails in production, you're accountable, not the AI. If that trigger has a bug that corrupts data, you can't tell your manager "the AI wrote it." If the component has a security vulnerability, "I didn't write that code" isn't a defense. You deployed it, so you're responsible for it.
This isn't about being unfair to developers using AI tools—it's about recognizing that deployment is an assertion of code quality. When you deploy code, you're stating that you've verified it meets your organization's standards. The method of generation doesn't change this responsibility.
This means your review process needs to be rigorous enough that you can defend the code's quality if questioned. Could you explain to a security auditor why this code is secure? Could you justify to your technical lead why this architecture makes sense? Could you defend to your manager why this code won't cause production incidents?
If the answer to these questions is "because the AI generated it and tests pass," that's insufficient. But if the answer is "I've verified it handles bulk data correctly, enforces security properly, and follows our architectural patterns," then you've done your job—regardless of whether you wrote it or AI generated it.
What This Means for Developer Skills
Some developers worry that relying on AI-generated code will atrophy their coding skills. This concern makes sense—skills you don't practice degrade. But I think it misunderstands what skills matter in this new development paradigm.
The skill that matters most isn't writing boilerplate code from scratch—it's knowing what good code looks like and how to assess whether code (from any source) meets quality standards. This is actually harder than writing code yourself, because you have to quickly evaluate code you didn't write without the context of having built it.
Think about what you need to know to effectively review AI-generated Salesforce code. You need to understand governor limits deeply enough to spot code that will hit them. You need to know security models well enough to identify gaps. You need to recognize current versus deprecated patterns. You need architectural judgment to assess whether code fits well into your existing systems. You need experience with production failures to anticipate what edge cases matter.
These are senior developer skills, not junior ones. Using AI effectively doesn't reduce the expertise required—it changes where that expertise is applied. Instead of spending time writing repetitive CRUD code, you're applying expertise to evaluate generated code and refine it for production use.
The developers who struggle with AI-generated code are often those who lack this evaluative expertise. They can write code following examples but can't easily assess whether someone else's code is good. Paradoxically, junior developers who might benefit most from AI assistance are least equipped to use it safely, while senior developers who least need the basic code generation are best positioned to use these tools effectively.
The Practical Middle Ground
Trust but verify, with verification focused on specific quality dimensions rather than comprehensive understanding of implementation details.
When the AI generates code, I immediately check the quality dimensions that matter for production: security, performance, error handling, edge cases, and architectural fit. This takes minutes rather than hours because I'm not studying every implementation choice—I'm checking specific concerns.
If the code passes these checks, I trust the implementation details even if I wouldn't have written it exactly that way. The AI might structure conditionals differently than I would, or choose variable names I wouldn't pick, or organize functions in an order I find unusual. These differences don't matter if the code is secure, performant, and maintainable.
If the code fails any of these checks, I either fix the specific issue or regenerate with a better prompt. Fixing is usually faster for small security gaps or performance tweaks. Regeneration makes more sense for fundamental architectural mismatches or when multiple issues suggest the AI misunderstood the requirement.
I document anything non-obvious. If the code handles an edge case in a specific way, or if there's a reason for a particular architectural choice, I add comments. This helps future maintainers (including future me) understand why things are the way they are, particularly for code that might look strange because it was AI-generated.
I treat AI-generated code as a first draft, not a final implementation. It's good enough to work from, rarely good enough to deploy without any modification. This mindset helps avoid both over-trusting (deploying without review) and over-scrutinizing (rewriting everything because it's not exactly how I would have done it).
What's Next
This question of understanding AI-generated code isn't going away—it's going to become more pressing as these tools become more capable and more widely used. Organizations will need standards for how thoroughly to review AI-generated code. Teams will need shared practices for assessing code quality regardless of source. Developers will need to develop the evaluative skills that matter more than ever.
For now, the key insight is this: understanding AI-generated code isn't binary. You don't need to understand it as deeply as code you wrote from scratch, but you do need to understand it well enough to verify it's secure, performant, architected appropriately, and handles edge cases correctly. That's a different type of understanding—more evaluative than implementational—but it's no less important.
Discussion Question: How do you decide whether AI-generated code is ready for production? What's your review process, and what quality checks matter most to you?
Read the full series:
- 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? (you are here)
Tags: #salesforce #agentforce #ai #vibecoding #salesforcedevelopment #codequality #softwaredevelopment
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