Deep engineering at Vstorm in the age of AI-code generation

The debate around vibe coding, using AI tools such as Claude Code to generate working software through natural language prompts, has grown louder and more polarized. On one side, advocates point to its speed and accessibility. On the other, critics warn of security vulnerabilities, the hidden technical debt AI adds, and codebases nobody can maintain. Both sides are absolutely right, and that is precisely where the problem lies.

Our work at Vstorm is dedicated to helping middle-market companies leverage Agentic AI in the right way on their journey to business transformation. AI-generated code, either produced by Claude Code or others, simply does not deliver, despite all the hype. Let me elaborate.

At Vstorm, we use AI code generation regularly. We use it for proofs of concept, rapid prototyping, ideation, and experiments where speed matters more than architectural discipline. We are not here to argue that vibe-coding is worthless. It is not. What we are here to argue is that vibe-coding risks are misunderstood by the people who matter most: mid-market operators investing in production-grade AI systems that need to work reliably, not just impressively in a demo.

The confusion arises because vibe-coding is genuinely excellent at the top of the development funnel… and genuinely dangerous at the bottom. Conflating the two is how expensive rescue missions are born.

Vibe-coding is a legitimate engineering tool

Let us be clear, we are not saying that vibe-coding itself is worthless. Vibe-coding tools have meaningfully changed early-stage development. In the right hands, vibe-coding can be cost effective in speeding up productivity on the road to critical implementations.

The problem begins when organizations attempt to carry that generated code through to production without the engineering work required to make it reliable, secure, and maintainable. That is not a vibe-coding problem. That is an expectations problem, and one we encounter often.

The hidden technical debt AI adds

A lot of projects we take on are what we internally call ‘rescue missions.’ These are more and more often situations in which an Agentic AI solution was put in place incorrectly and requires deep engineering work to alleviate the situation. It is easier for our team to improve the unfinished or flawed designs of humans. And it is far more demanding to reengineer a system that has its code generated.

The reason for this is what we call ‘Technical Debt,’ which accumulates quickly in complex Claude-generated systems, where deep engineering is disregarded.

When using an AI like Claude to generate code, often called vibe-coding, technical debt accrues because the AI is optimized for “making it work right now,” fulfilling your request at the moment, while disregarding the necessary deeper understanding of the impact in the long term of maintaining or evolving the code.

Think of Technical Debt as a high-interest loan on development time. When you fast-track your system by vibe-coding, you “spend” that credit quickly. What you receive is a working system today, in exchange for a system that is expensive to debug, difficult to evolve, and opaque to the team responsible for maintaining it down the line. Eventually, you will have to pay that credit back with interest (extra work) later.

This knowledge gap is not a training failure. It is an architectural one: when code was not written by a human who understands it, it cannot readily be understood by a human who must maintain it.

And this problem compounds in agentic AI implementations. Agentic systems operate across a variety of tools, APIs, databases, and decision loops. A structural flaw in one node propagates through the entire system. A standard web application with a security flaw is an embarrassment, but an agentic AI system deployed on mission-critical workflows with a structural flaw is a liability.

“Vibe coding your way to a production codebase is clearly risky. Most of the work we do as software engineers involves evolving existing systems, where the quality and understandability of the underlying code is crucial.”
– Simon Willison, independent software developer and AI researcher, in Ars Technica

What production-grade AI systems actually require

So what is included in the payslip when you hire real engineers, often PhDs, to deliver your solution? There are five things deep engineering work provides that AI code generation does not, and all five matter when a system operates in production:

Context. Our TriStorm methodology ties business objectives directly to system architecture. An agentic system built without that alignment will work in isolation and fail in production, where real business processes are messier than any prompt can capture. Engineers will not lose that from sight, while context-blind code generators can.

A glass box, not a black box. AI code generators are capable of producing complex, nested logic that passes tests but which also all too often creates a black box. Engineers produce code that can be read, understood, debugged, and extended by anyone on the team, not only the person present when it was written.

Simplicity. The same outcomes can often be achieved with a fraction of the complexity that AI generation produces. It is easy to bedazzle with loads of AI-generated code that works, but it is even more impressive to see a simpler, more robust and elegant path. That, however, always requires understanding the problem deeply enough to recognize that option exists. As Steve Jobs is often credited to describing it, simplicity is the ultimate sophistication.

Safety. Engineers can vouch for security, as they can think through the consequences: first order, second order, or third order. They can push back on an architectural decision and propose more secure alternatives,suggesting a different approach or changing methods. A code generator optimized to satisfy the prompter will not do this, and simply cannot.

Genuine understanding. This is the most significant differentiator. Deep engineers understand and care about the outcome. AI code generators produce plausible outputs. These are not the same thing, and the difference matters most when something goes wrong in production. This comes down to the question of code completion (where vibe-coding does well) and coding practical AI agents in production (where it falls short).

What we see in practice: the trap

“Why don’t you supercharge your coding? The proposals I receive from your competitors are more efficient as they lean on AI support more,” something we hear from time to time. It shows a pattern some of our potential customers fall into that we want to warn you about.

A significant share of the engagements we take on at Vstorm are what we call rescue missions: projects where an agentic AI solution was implemented without deep engineering work and has failed to perform reliably in production. Correcting flawed human architecture is demanding, but reengineering systems where the code was generated and the original developer cannot explain how it functions is considerably more so.

We use AI-code-generated parts in all of our projects, from proofs of concept, ideation, experiments, or quick prototypes all the way to production-grade systems. But we do not solely rely on it for production-grade implementations, which are required to work reliably without the risk of hallucination. While vibe-coding is an important tool in any effective engineer’s toolbox, like a hammer, it cannot be counted on to build complex workflows on its own. Achieving that in the realm of stochastic technologies (what LLMs are) requires a proper handle on the importance of deep engineering work.

We have seen small companies building financial support systems supercharged by AI-generated coding. Helping in that is not our cup of tea. But working on high-reliance, high-quality AI support is. Our hybrid agent-graph architecture case study illustrates what that engineering work looks like in practice: a deliberate, iterative transition from a single-agent system to a production-grade AI system built on Pydantic AI and Text to SQL, with each architectural decision justified and documented. That kind of work cannot simply be generated. It has to be thought through.

The right division of labor

The most effective way to use AI code generation is as an accelerator at the front of the development process and human expertise as the gate at the back. That is also how we use it at Vstorm.

AI-generated code is well-suited to generating boilerplates, scaffolding initial module structures, producing test cases for well-defined behavior, and accelerating the exploration of solution patterns. Used in those contexts, it reduces time without losing quality. We explore this further in our article on the use of AI by AI engineers.

Production-grade AI systems require human engineers to define the architecture, assess security implications, validate behavior across edge cases, write code others can read and maintain, and make the judgment calls no prompt can capture, including the judgment to say that a proposed approach is wrong.

Startups have the luxury of making a different calculation. They need to move fast with limited resources, and the cost of technical debt is acceptable when survival depends on immediate market feedback. Established mid-market businesses operating mission-critical processes cannot accept the same trade-off. The consequences of an agentic system failing in production, such as in healthcare, in financial services, or in complex fulfillment operations, are not recoverable with a hotfix.

At Vstorm, we do not build production-grade AI systems entirely on generated code. Not because the tools are poor, but because our clients’ processes deserve better than unsupported optimism. They deserve to be understood.