Do You Need to Understand AI-Generated Code?

Source: Dev.to

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?”

Why this matters now

This isn’t a theoretical question any longer. 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.
• 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.

Two opposing viewpoints

Both perspectives contain truth, but both miss something important. The question isn’t whether to understand AI‑generated code—it’s what aspects you need to understand, and how deeply, to deploy it responsibly.

The trigger example – levels of understanding that matter

The developer who deployed the trigger without deep review likely understood the business logic: when an Account changes, update related records in a specific way. Tests confirmed this behavior worked. However, production code demands additional dimensions of understanding.

You don’t need to know why the AI chose a particular variable name or whether a slightly more elegant algorithm exists. You do need to know whether the code is secure, performant, architecturally appropriate, and robust. These are different types of understanding, requiring different review approaches.

A systematic review approach

Ad‑hoc review is insufficient; it’s too easy to assume the AI got everything 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 translates into a concrete checklist. Below is a concise, five‑minute checklist that catches the majority of production‑blocking problems in AI‑generated Apex code.

Checklist for AI‑generated Apex code

1. Bulkification

   • Does the code process collections properly, or does it query/DML inside loops?
   • Mentally run it with 200 records—does it stay within governor limits?

2. Security

   • Does it run with proper sharing (with sharing / without sharing)?
   • Does it respect field‑level security?
   • Could users access data they shouldn’t through this code?

3. Governor limits

   • Beyond bulkification, are there other limit risks?
   • Total SOQL queries, DML statements, CPU time, heap size, etc.

4. Error handling

   • Are exceptions caught appropriately?
   • Will users see helpful error messages or mysterious failures?

5. Edge cases

   • What if data is missing, malformed, or unexpected?
   • Does the code fail gracefully or crash?

6. Architectural fit

   • Does it follow your trigger framework or established patterns?
   • Will it conflict with existing automation (other triggers, Process Builder, Flow, etc.)?

Production‑Readiness Assessment (Not a Full Code Review)

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.

Lightning Web Components (LWC) Checklist

The checklist for LWC is different but equally systematic. Ask yourself:

• 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.

How Agentforce Vibes Changed My Review Process

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.

1. Early stage – I reviewed everything in detail because I had no intuition about AI‑generated code quality.

2. Pattern recognition – I discovered that the AI is remarkably good at:

   • Basic CRUD operations
   • Straightforward business logic

   It is less reliable at:

   • Performance optimization
   • Security enforcement
   • Handling edge cases

   The AI almost always generates syntactically correct code but frequently uses outdated patterns that work yet aren’t current.

3. Targeted focus –

   • When the AI generates a simple query‑and‑display component, I 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 mirrors how you might review code from different team members: trust experienced developers with light review, give junior developers more detailed feedback. The difference with AI is that you’re building a mental model of its capabilities and limitations rather than modeling 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.

Situations Requiring Deeper Understanding

These aren’t arbitrary; they’re contexts where the cost of problems is high or shallow understanding creates specific risks.

• Sensitive data / security‑critical code – Verify not only that security checks exist, but that they’re implemented correctly and cover all access paths.
• External system integrations – API integrations, third‑party callouts, and data‑synchronization logic often have subtle requirements that aren’t obvious from tests. Understanding the integration helps you anticipate breakage when external systems change.
• Maintainability for the team – Code that works but is confusing creates maintenance problems. Refactoring AI‑generated code may be worthwhile not because it’s wrong, but because your team won’t be able to maintain it effectively.
• Complex business logic – When requirements are likely to change, you need to understand how the AI implemented a complex calculation or workflow.
• Performance‑critical code – If a component must 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.

Accountability: The Uncomfortable Truth

When AI‑generated code fails in production, you’re accountable, not the AI.

• If a trigger has a bug that corrupts data, you can’t tell your manager, “the AI wrote it.”
• If a component has a security vulnerability, “I didn’t write that code” isn’t a defense.

You deployed it, so you’re responsible for it.

Defending the Code’s Quality

Your review process must be rigorous enough that you can defend the code 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 is “because the AI generated it and tests pass,” that’s insufficient.
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 who wrote the code.

Skill Atrophy Concerns

Some developers worry that relying on AI‑generated code will atrophy their coding skills. The concern makes sense—skills you don’t practice degrade. However, this misunderstanding overlooks what skills matter in the new development paradigm:

The most important skill isn’t writing boilerplate from scratch; it’s knowing what good code looks like and how to assess whether it meets quality, security, performance, and maintainability standards.

By focusing on these evaluation skills, you can continue to add value even when much of the code is generated by AI.

Do You Need to Understand AI‑Generated Salesforce Code?

You are here – Part 3 of the series

Why Reviewing AI‑Generated Code Is Harder Than Writing It

• You must quickly evaluate code you didn’t write without the context of having built it.
• To do this effectively you need to know:
  1. Governor limits – deep enough to spot code that will hit them.
  2. Security models – to identify gaps before they become vulnerabilities.
  3. Current vs. deprecated patterns – to avoid future maintenance headaches.
  4. Architectural judgment – to decide whether the code fits into existing systems.
  5. Production‑failure experience – to anticipate edge‑cases that matter in real use.

These are senior‑developer skills, not junior ones. Using AI doesn’t eliminate the expertise required; it simply shifts where that expertise is applied.

The Paradox of Junior vs. Senior Developers

• Junior developers can generate code from examples, but often lack the evaluative expertise to judge whether that code is production‑ready.
• Senior developers may need less basic code generation, yet they are best positioned to evaluate and refine AI‑generated output.

“Trust but verify, with verification focused on specific quality dimensions rather than a comprehensive understanding of every implementation detail.”

My Review Process (Minutes, Not Hours)

1. Identify the quality dimensions that matter for production:

   • Security
   • Performance
   • Error handling
   • Edge‑case coverage
   • Architectural fit

2. Run a rapid checklist against those dimensions.

   • If the code passes, I trust the implementation details even if they differ from my personal style.
   • If it fails, I either:
     • Fix the specific issue (e.g., a small security gap or performance tweak).
     • Regenerate with a better prompt when the problem is more fundamental.

3. Document anything non‑obvious:

   • Add comments for edge‑case handling or unusual architectural choices.
   • This helps future maintainers (including yourself) understand why the code looks the way it does.

4. Treat AI‑generated code as a first draft:

   • Good enough to work from, rarely good enough to deploy unchanged.
   • This mindset avoids over‑trusting (deploying without review) and over‑scrutinizing (rewriting everything).

The Bigger Picture

• The need to understand AI‑generated code won’t disappear; it will become more pressing as tools improve and adoption grows.
• Organizations will need standards for how thoroughly AI‑generated code is reviewed.
• Teams will develop shared practices for assessing code quality regardless of source.
• Developers must cultivate evaluative skills that are now more critical than ever.

Key insight: Understanding AI‑generated code isn’t binary. You don’t need to know every line as deeply as code you wrote from scratch, but you must verify that it is secure, performant, architecturally sound, and handles edge cases correctly. This is a different, more evaluative type of understanding—still essential.

Discussion Question

How do you decide whether AI‑generated code is ready for production?
What does your review process look like, and which 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