Vibe Coding Reality Check: What 2 Months of AI-Assisted Enterprise Development Taught Me | KMS ITC | KMS ITC

A few months ago, I was optimistic about AI-assisted coding. I thought it would finally alleviate the burden on developers. Fast forward to today, and my perspective has shifted dramatically. After deploying AI coding on a complex, high-stakes enterprise project, I’m here to share the unvarnished truth about what “vibe coding” really means in production environments.

Spoiler alert: It’s not what the demos promised.

The Paradox Nobody Talks About

Here’s the uncomfortable truth: after adopting AI coding, my working hours actually increased. I went from normal hours to 10 AM - 10 PM at the office, then continuing until the early morning hours at home. About a third of my workdays extended past midnight.

When I first started using AI coding tools, I genuinely believed they would reduce our workload. Instead, I found myself more exhausted than ever. This paradox—where a productivity tool makes you less productive—deserves serious examination.

The Project: High Stakes, No Room for Error

Let me set the context. We used Specification-Driven Development (SDD) with AI assistance for:

Architectural design
System development
Unit testing and integration testing

The project itself was a complex, multi-layered architecture involving:

Financial transactions — errors cost real money
High-traffic scenarios — think large-scale promotional events
Strict availability requirements — downtime is unacceptable
Concurrent access and data consistency — distributed systems complexity
Strict deadlines — the schedule was immovable

To illustrate the complexity: a simple external API call required:

Synchronous interface call
Message-based compensation for timeouts
Scheduled tasks for order reconciliation (minute-level, hour-level, day-level)
Technical contingency plans for forced state synchronization
Customer service support documentation

That’s the reality of enterprise development. Every “simple” feature has tentacles reaching into monitoring, compensation, reconciliation, and support systems.

Phase 1: Architecture — Impressive But Dangerous

We had the AI assist with architecture and design work. The output looked incredibly professional—better than what most humans would produce. The documentation was thorough, detailed, and well-structured.

But there were warning signs we ignored:

The Verbosity Problem

The AI loved writing exhaustive documentation. Where humans rely on shared context and company conventions that don’t need explicit documentation, the AI wrote everything out. The result was documents that seemed slightly repetitive but subtly different in ways that mattered.

The Review Gap

Because we were under delivery pressure, we didn’t carefully examine every detail of the AI’s architectural subdivisions. We rushed into coding.

This was our first critical mistake.

All the hidden problems in the architecture phase exploded during implementation, putting our entire team in a defensive position.

Phase 2: Development — Fast But Flawed

Due to data security requirements, we couldn’t let the AI access our existing code repositories. We created a fresh project and manually wrote example code showing:

How we define external service interfaces
How we consume external services
How layers interact in our DDD architecture
How we handle internal messaging
Our scheduled task frameworks

Armed with this knowledge, the AI’s development speed was breathtaking:

What would take a human engineer 15-20 person-days took the AI approximately 3 days.

The initial code looked impressive:

Detailed class diagrams with clear dependencies
Comprehensive code comments
Thorough exception handling
Straightforward business logic (no over-engineered abstractions)

Then reality hit.

The “Garbage In, Garbage Out” Principle

When I reviewed the first version, I found extensive fabricated logic. The AI either:

Didn’t have information and made things up
Received incorrect documentation and perpetuated the errors
Encountered missing interface definitions and guessed the format

This is fundamental to how AI works. If your input contains errors—even in comments or documentation—the output will confidently propagate those errors.

The Testing Trap

The AI wrote extensive unit tests and integration tests. They all passed. We felt confident.

But here’s the problem: The AI designed the tests based on its flawed understanding of the requirements. It implemented code from a flawed system analysis document, then created test cases based on that same flawed design.

Of course it couldn’t find its own bugs—it was validating its misunderstandings against themselves.

Phase 3: Debugging — Where Dreams Die

Then came the most painful phase: point-by-point debugging.

The code that initially looked clean and well-structured gradually transformed into a tangled mess as we fixed issue after issue. Each fix introduced new edge cases. Each edge case required more patches.

The Self-Review Delusion

We had the AI perform code review on its own work. It gave itself a 98/100 score, confidently declaring the code “nearly perfect.”

When humans started reviewing:

Low readability after extensive patching
Non-compliance with team development standards
Unfamiliar patterns for transaction handling
Hidden risks we couldn’t easily identify

We spent hours debating certain design decisions, eventually concluding “this approach is acceptable”—but the fact that humans needed extensive discussion to understand the AI’s reasoning created profound unease.

The Nuclear Option

Our final decision: rewrite the core logic from scratch.

Not because the AI couldn’t code, but because we couldn’t trust code we didn’t fully understand. We provided template classes and example code to constrain the AI’s output, essentially using it as an intelligent autocomplete rather than an autonomous developer.

The project shipped successfully. But at what cost?

Root Causes: Why AI Coding Struggles at Scale

1. Probabilistic Nature

Current transformer-based models are fundamentally probabilistic. Even with identical inputs, identical workflows, and identical prompts, the output varies. This non-determinism is acceptable for creative writing but terrifying for financial systems.

2. Context Window Limitations

AI constantly compresses information. When solving a specific problem, it sometimes “forgets” other design constraints. This manifests as:

Code duplication — writing the same logic multiple times for similar problems
Inconsistent patterns — different approaches for identical scenarios
Orphaned comments — forgetting to update documentation after code changes
Stale tests — failing to update test cases when requirements change

3. Missing the Forest for the Trees

AI excels at solving the immediate problem in front of it. It struggles to maintain awareness of how that solution fits into the broader system architecture.

4. The Expectation Inflation Problem

Here’s an insidious side effect: AI made leadership believe our productivity should skyrocket. The result? We weren’t working on one project—we were juggling multiple projects simultaneously, each with its own AI coding sessions.

My brain couldn’t handle the constant context switching. I found myself passively accepting whatever the AI suggested rather than critically evaluating it.

When I pointed out problems, the AI would say “You’re right!” Then implement a fix. Then I’d discover it created new problems. The cycle was exhausting.

The Constraint Spiral

To improve results, we added more constraints:

Different prompts for different development phases
Defined agents to control behavior
Workflows to prevent divergent outputs

But this created a vicious cycle:

Problem occurs → Add constraint
New problem occurs → Add more constraints
Too many constraints → AI makes different mistakes
Response → Add even more constraints

It’s like the old CS joke: “If a problem can’t be solved, add another layer of abstraction.” For AI, this became: “If an agent misbehaves, add another agent to supervise it.”

The result? Bloated workflows, hundreds of millions of tokens consumed daily, and costs spiraling out of control.

We were adding water when there was too much flour, then adding flour when there was too much water.

What AI Coding Actually Does Well

I don’t want to be purely negative. After this experience, I’ve recalibrated my expectations. AI coding genuinely excels at:

Understanding Unfamiliar Codebases

Starting a new job, AI helped me understand existing code quickly. It could explain patterns, find bugs, and analyze problems faster than reading documentation.

Pair Programming

For daily tasks, AI is an excellent thinking partner. It suggests approaches, catches obvious errors, and accelerates routine development.

Code Review Assistance

AI can identify potential risks in architecture designs and refactoring plans. It’s not a replacement for human review, but it’s a useful first pass.

One-Off Scripts

Need to process a data report? Write complex SQL queries? Generate charts? AI handles these beautifully because the stakes are lower and the context is contained.

Test Generation

Given clear specifications, AI writes comprehensive test cases faster than humans. Just don’t rely on it to test its own assumptions.

The Verdict: Know Your Boundaries

For individual developers building apps, personal websites, or prototypes—AI coding is fantastic. Go wild.

For enterprise systems with high traffic, financial implications, and availability requirements—proceed with extreme caution. Expect significant human oversight. Don’t believe the zero-human-input fantasy.

The fundamental challenges remain:

Context window limitations
Probabilistic outputs
Inability to truly understand domain conventions
The “confident but wrong” problem

Until these are solved—and as of January 2026, they’re not—enterprise development requires humans in the loop at every critical decision point.

Lessons for Teams Adopting AI Coding

Don’t skip architecture review — Pressure to ship doesn’t justify trusting AI-generated designs without thorough human examination.
Provide high-quality examples — AI’s output quality is bounded by input quality. Garbage in, garbage out.
Never let AI test its own designs — The same misunderstandings that produce bugs will produce tests that miss those bugs.
Maintain realistic expectations — AI is a powerful tool, not a replacement for engineering judgment.
Plan for rewrites — Budget time for humans to rewrite critical sections. It’s not a failure; it’s pragmatism.
Resist the constraint spiral — More rules don’t always produce better results. Sometimes they produce different failures.
Protect your developers — Don’t let “AI will make us faster” become an excuse to overload your team with simultaneous projects.

Final Thoughts

AI coding is genuinely powerful. It has improved my productivity in specific contexts. But the vision of AI autonomously producing production-ready enterprise code remains a fantasy.

The most valuable insight from this experience? AI coding amplifies your process, for better or worse. If your architecture review is solid, AI accelerates good outcomes. If you skip reviews under deadline pressure, AI accelerates your path to disaster.

Use AI as a force multiplier for good engineering practices, not as a substitute for them.

Navigating AI adoption in your development process? Contact KMS ITC for guidance on integrating AI tools effectively while maintaining enterprise-grade quality standards.