There's a complaint I see constantly from developers:
"AI wrote the code but I spent hours debugging it."
I've been using AI heavily in my own projects. And honestly — I don't relate to this experience. Not because the AI I use is magical, but because I've noticed the bugs almost always trace back to one of two things. Neither of them is the AI.
Problem 1 — The Implementation Plan
When I give an AI agent a vague instruction, I get vague code back. That's not a bug. That's math.
If I say:
"Build me an authentication system"
I'm going to get generic code that makes assumptions about my database structure, my session handling, my token strategy, and my error responses. Some of those assumptions will be wrong. I'll spend an hour figuring out which ones.
If I say:
"Build a token-based auth system using Laravel Sanctum. Users table already exists with these columns. Tokens stored in personal_access_tokens. Login returns a Bearer token. Failed login returns 401 with this JSON structure. No session-based auth needed."
The output is precise. The edge cases are handled. There's almost nothing to debug.
The AI didn't get smarter. I gave it a better plan.
This is the part most developers skip. They open a chat, type a rough idea, and then blame the tool when the output doesn't match the thing they had in their head but never wrote down.
An AI agent is not a mind reader. It's a very fast executor of whatever context you give it. The quality of the output is almost entirely a function of the quality of the input.
Problem 2 — Session Length
This one is less talked about but equally important.
AI models have a context window. As a conversation gets longer, earlier parts of it get compressed or effectively lost. The model starts making decisions without full access to what was established at the start of the session.
In practice this looks like:
- A function that contradicts a constraint you defined 40 messages ago
- A variable name that doesn't match the naming convention from earlier in the project
- Logic that reimplements something already handled elsewhere in the codebase
The code isn't wrong because the AI is bad. It's wrong because you asked it to hold more context than it reliably can.
The fix is simple: start a new session when the context gets heavy.
Before a new session, summarize what's been built, what conventions are in use, what's already handled, and what the next task is. Paste that as the first message. You're not losing work — you're giving the AI a clean working memory instead of a cluttered one.
Developers who don't do this and then hit inconsistencies blame the AI. Developers who manage their sessions rarely hit those inconsistencies.
Where AI Actually Deserves the Blame
I want to be honest here — there are cases where the AI genuinely introduces bugs regardless of how good your plan is.
Complex interdependent business logic is one. If you have five systems that affect each other in non-obvious ways, AI will occasionally make a confident assumption that's subtly wrong. It won't tell you it's guessing. It'll just be wrong.
Large existing codebases are another. Asking an AI to extend code it hasn't seen in full leads to inconsistencies. It's working with a partial picture.
These cases exist. They're real. But in my experience they're maybe 10-20% of the debugging complaints I see developers make. The other 80% is a planning problem or a session management problem — both of which are entirely within the developer's control.
"But Real Development IS Complex Tasks"
Fair pushback — and I've heard it.
The argument is: simple tasks are easy to plan, but real-world development involves complex interdependent systems. That's where AI falls apart, and no amount of planning fixes that.
I partially agree. But the response is not "therefore AI can't handle complex work." The response is: complex tasks are just multiple small well-defined tasks stacked together.
The mistake is treating complexity as one big instruction:
"Build me a multi-tenant payroll engine with attendance, leave, deductions, and statutory compliance"
That's not a task. That's a project. Throwing that at an AI in one session and expecting clean output is not a workflow problem — it's an expectation problem.
Break it down:
Session 1 — Tenant database switching on auth
Session 2 — Employee model and relationships
Session 3 — Attendance calculation logic
Session 4 — Payroll computation engine
Session 5 — Statutory deductions
Each session gets a fresh context. Each task is specific. The AI's output per session is clean because the scope is clear.
The complexity doesn't disappear — you're managing it. That's the actual skill. Decomposing a complex system into well-scoped pieces and feeding them to an AI with proper context is harder than typing one big prompt. It's also the reason some developers get great results and others don't.
Complex tasks don't break the argument. They prove it.
What I Actually Do
Before any non-trivial task I write out:
- What already exists and what it does
- What I'm building and why
- The data structures involved
- Edge cases I already know about
- What the output should look like
Then I start a fresh session with that as context.
When a session gets long — past maybe 20-30 exchanges on a complex task — I start a new one with a summary.
This workflow basically eliminated the "AI wrote broken code" problem for me. Not because I'm using it better than everyone else — just because I stopped treating it like a shortcut and started treating it like a junior developer who needs a proper brief.
A junior developer given a vague task will produce vague work. The same junior developer given a clear spec, existing context, and defined constraints will produce something usable.
The AI is the junior developer. The brief is your job.
I write about practical decisions behind real projects. Follow if that's useful.
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