AI Can Write Your Code. Can It Actually Debug It?

An engineer discusses the challenges of using AI for debugging code, contrasting it with code generation. The post highlights that AI coding assistants excel at writing code but struggle with investigating failures, which requires tracing execution paths and understanding application state. The author suggests that effective AI debugging should identify root causes and reproduce bugs, not just explain error messages.

AI coding assistants have changed how developers write software. You can describe a feature, generate a function, refactor a component, write a test, or explain an unfamiliar codebase in seconds. But there is one part of software development that is still surprisingly difficult: figuring out why something broke. Writing code and investigating a failure are two very different problems. When an application crashes, the answer usually isn't sitting inside the error message. You have to reconstruct what happened. Consider this Node.js error: TypeError: Cannot read properties of undefined reading 'email' at getUser /app/services/user.js:42:18 at processRequest /app/controllers/auth.js:87:12 at async handler /app/routes/auth.js:31:5 The immediate problem appears obvious. Something is undefined. But what caused it? Maybe: The stack trace tells you where the program finally failed . It doesn't necessarily tell you where the bug began . That's the difference between error reporting and debugging investigation. Most AI coding workflows look something like this: Developer ↓ Prompt ↓ AI ↓ Code Debugging is different: Failure ↓ Error ↓ Stack trace ↓ Execution path ↓ Application state ↓ Root cause ↓ Fix The AI needs to reason across that chain. Simply asking: "What does this error mean?" usually produces a list of possible explanations. That's useful, but it's not necessarily an investigation. A better question is: "Given this failure and its context, what is the most likely root cause, what evidence supports it, and how can I reproduce it?" That's a much more interesting problem for AI. Imagine this code: js async function getProfile userId { const user = await getUser userId ; return { name: user.profile.name, email: user.profile.email }; } And the application crashes here: user.profile.email A quick fix might be: if user { return null; } But that might not solve the actual problem. Why is user missing? Maybe getUser is querying the wrong database. Maybe the user ID comes from an expired authentication token. Maybe a deleted account is still referenced somewhere. Maybe an API changed its response format. Maybe the application has a race condition. The correct debugging process is to trace the value backward. user.profile.email ↑ user ↑ getUser userId ↑ authenticated ID ↑ incoming request The goal is to find the first point where reality differs from what the program assumes. A good debugging workflow should answer more than: What line crashed? It should answer: Identify the exact exception and operation. Find the relevant file, function, and line. Reconstruct the call chain. Inspect the values flowing through the system. This is often where the real bug becomes visible. A reproducible bug is much easier to fix confidently. The solution should address the cause rather than simply hiding the exception. TypeScript prevents many classes of bugs. But external data still exists outside the type system. For example: js interface User { id: string; email: string; } const response = await fetch "/api/user" ; const user = await response.json as User; This looks safe. But this: as User doesn't validate the actual response. The server could return: { "id": "123" } and the runtime value still doesn't contain email . This is one reason TypeScript applications can still have confusing runtime errors. Compile-time types describe what we expect. They don't guarantee that every external system behaves according to those expectations. Modern JavaScript applications are heavily asynchronous. A single request might pass through: HTTP request ↓ Middleware ↓ Controller ↓ Service ↓ Database ↓ Third-party API ↓ Transformation ↓ Response The visible exception could happen at the end of this chain while the original problem happened several steps earlier. That's why debugging complex Node.js applications often feels like detective work. You're reconstructing a sequence of events from incomplete evidence. When you encounter a difficult Node.js error, don't immediately change random lines of code. Start with the evidence. Keep the complete: Avoid reducing a complex error to something like "Node is crashing." The details matter. Identify the exact file, function, and line where the exception occurs. Then inspect the surrounding code. Determine which functions called the failing function. For example: API handler ↓ Controller ↓ Service ↓ Repository ↓ Database Look at the values being passed between those functions. Ask: Which value isn't what the code expected? The line that crashes is not always where the bug started. The most useful question is: Where did the application first enter an unexpected state? Try to identify the exact conditions that trigger the bug. A reliable reproduction is often more valuable than ten guesses about the cause. Don't simply suppress the exception. Make the application correctly handle the state that caused it. Once the bug is fixed, make sure it cannot silently return. There's a huge difference between these two approaches. Maybe the database is broken. Try restarting the server. The database query returns no record for users created without a profile. The service assumes the record exists and then accesses profile.email without validation. The second one is actionable. Good debugging isn't about generating the largest number of possible causes. It's about reducing uncertainty until one explanation is supported by evidence . This is where AI-assisted debugging gets interesting. Instead of using AI only as a code generator, you can use it as an investigation assistant. For example, provide: Then ask the AI to reason through the evidence. A useful investigation might produce something like: Error ----- TypeError: Cannot read properties of undefined Location -------- userService.ts:87 Likely root cause ----------------- getProfile assumes every user has a profile, but newly created users can exist without one. Evidence -------- The profile lookup returns undefined for users created before profile initialization. Reproduction ------------ 1. Create a new user. 2. Skip profile initialization. 3. Request /profile. 4. Access profile.email. Recommended fix ---------------- Validate the profile result before accessing nested properties and add a regression test. That's considerably more useful than: "You should check whether profile is undefined." AI code generation asks: "What code should I write?" AI debugging asks: "What happened?" And then: "Why did it happen?" And finally: "What evidence supports the explanation?" This distinction matters. A coding assistant can generate a plausible fix without necessarily understanding the complete system failure. A debugging investigation should aim to connect: symptom → execution path → state → violated assumption → root cause → fix I've been building KaudDoc , an AI-powered deep code investigation tool around this problem. The idea is simple: Don't stop at explaining the error. Investigate the failure. Instead of only asking: "What does this stack trace mean?" the goal is to get closer to: "What actually happened, why did it happen, and what should I investigate next?" KaudDoc is designed around debugging investigations involving things such as: The goal isn't to replace your IDE debugger. It's to provide another layer of investigation when a normal error message or stack trace isn't enough to understand a complicated failure. A useful investigation shouldn't just produce a wall of AI-generated text. It should organize the evidence. For example: ┌─────────────────────────────┐ │ ERROR │ │ TypeError │ └──────────────┬──────────────┘ ↓ ┌─────────────────────────────┐ │ EXECUTION PATH │ │ API → Controller → Service │ └──────────────┬──────────────┘ ↓ ┌─────────────────────────────┐ │ FAILED ASSUMPTION │ │ Profile always exists │ └──────────────┬──────────────┘ ↓ ┌─────────────────────────────┐ │ ROOT CAUSE │ │ Missing profile record │ └──────────────┬──────────────┘ ↓ ┌─────────────────────────────┐ │ RECOMMENDED FIX │ │ Validate profile + test │ └─────────────────────────────┘ The value is not just the final answer. The value is the reasoning path that connects the evidence to the conclusion. Local debugging is relatively easy. You can: Production debugging is different. You may have: This is where structured investigation becomes particularly useful. A production debugging workflow should help answer: I think we're going to see a shift in how AI developer tools are built. The first generation focused heavily on: "Write this code for me." The next generation can go deeper: "Understand this failure and help me investigate what happened." Those are fundamentally different tasks. Code generation is primarily about producing an output. Debugging is about reasoning from evidence. And real-world software failures rarely come with perfect information. You might have one stack trace, several logs, a large codebase, and a bug that happens once every few hours. That's where an AI system that can investigate rather than simply autocomplete becomes interesting. The next time you encounter a difficult JavaScript, TypeScript, or Node.js error, use this checklist: Capture the complete error Read the entire stack trace Identify the failing operation Reconstruct the execution path Trace the relevant data Find the first invalid assumption Reproduce the failure Identify the root cause Apply the smallest correct fix Add a regression test Verify the fix This process works whether you're debugging a small JavaScript project or a large Node.js application. The best debugging question isn't: "How do I make this error disappear?" It's: "Why did the system get into this state?" Once you can answer that question, the fix usually becomes much clearer. AI has become remarkably good at helping developers write code. The next interesting challenge is making it equally useful when the code doesn't behave the way we expected. Debug the symptom. Investigate the system. Find the cause. If you're interested in AI-assisted debugging and deep code investigation, KaudDoc is the project I'm building around this idea.