Agentic Engineering Is More Than Vibe-Coding
Why Quality Is Not Determined by the Model, but by the Guardrails
Over the past months, a particular narrative around AI-driven software development has taken hold.
AI writes code — fast, impressive, sometimes surprisingly good. Terms like vibe-coding, agentic coding, and agentic engineering are often used interchangeably, as if the main question were simply how much code a model can generate.
From my practical experience, I consider this equation misleading.
Not because AI-generated code is inherently bad.
But because it misses the real issue.
Different outputs are not a quality problem — missing guardrails are.
This idea is central to understanding what agentic engineering is actually about.
The Misunderstanding Around Quality
One of the most common arguments against AI-assisted development goes like this:
“If I run the same prompt ten times, I get ten different versions of the code. How can you ensure quality that way?”
The argument sounds plausible — but it is incomplete.
The same phenomenon exists in classical software development. Give ten developers the same task, and you will get ten different implementations. Different structures, different trade-offs, different decisions. Yet we do not describe this as unreliable development.
The difference lies in the practice itself. Quality does not emerge from uniformity, but from consciously defined standards: coding conventions, architectural principles, review rules, and clear ownership. These self-imposed guardrails ensure that different solutions still meet a consistent level of quality.
The same principle applies to agentic systems.
Agentic Engineering Starts Before the Code
The most significant difference between vibe-coding and agentic engineering lies in where people believe the work begins.
Vibe-coding starts with the prompt.
Agentic engineering starts much earlier.
In practice, implementation is rarely the bottleneck. AI can generate code extremely fast; software itself is no longer the limiting factor.
The real differentiator is the product decision:
What are we building?
Why?
For which problem?
Based on which assumptions?
Product practice has never been just backlog management. But in a world where execution speed is no longer scarce, it becomes the decisive competitive factor. Teams that fail to frame problems clearly will produce a lot of software very quickly — just not necessarily the right software.
Artifacts as Control, Not Bureaucracy
Agentic engineering requires explicit artifacts. Not to inflate process, but to make decisions, assumptions, and quality manageable.
In practice, a clear flow has proven effective:
- Product requirements describing the problem space, hypotheses, and capabilities
- Prototypes as business references to validate assumptions
- Asynchronous refinement to establish clarity
- Architecture specifications as the handoff to the agentic system
These artifacts are not ends in themselves. They exist to make context explicit — for humans as well as for models.
Architecture Is the Real Prompt
The decisive transition into agentic work does not happen at the level of code, but at the level of architecture.
Models are capable of generating plausible architectural proposals based on the available context. Precisely for that reason, an architecture specification is not an output to be accepted blindly, but a draft that must be reviewed, refined, and validated.
System boundaries, legacy dependencies, implicit risks, and technical standards cannot be delegated. Responsibility here clearly remains with the developers.
Implementation only begins once this architecture has been consciously approved.
This is not a formality — it is one of the most important guardrails.
Responsibility Does Not Disappear — It Shifts
In agentic setups, developers write less code themselves. What they do not lose is responsibility.
Instead, responsibility moves earlier in the process. Rather than focusing primarily on implementing tasks, developers evaluate assumptions, review architectural decisions, and continuously verify whether implemented capabilities actually produce the intended behavior.
In complex or long-lived systems, experience becomes even more important. Implicit knowledge, judgment, and contextual understanding cannot be automated.
Agentic engineering is not a shortcut around experience.
It Feels Slower — And Yet It Is Faster
This approach initially feels heavier: more upfront work, more review, more structure. In practice, however, a more nuanced picture emerges.
Depending on system complexity and the degree of legacy dependencies, we observe efficiency gains of roughly 50 to 80 percent, while maintaining consistent quality. In general, the more complex the system and the stronger the legacy constraints, the greater the impact of clear guardrails and deliberate upfront work.
Speed here is not the result of haste, but of clarity.
Not Dogma, but Context Sensitivity
Agentic engineering is not a rigid framework. Not every change requires the same level of upfront effort.
A small change can be handled with a clear ticket and a targeted prompt. A new feature in a complex legacy system, however, requires explicit product and architectural work. What matters is not the number of artifacts, but the context in which they are applied.
A Practice in the Making
Agentic engineering is not yet an established discipline with textbooks and fixed rules. It is emerging — in practice, not in tool demos.
Many people talk about agentic engineering while actually referring to vibe-coding. The difference, however, does not lie in the model, but in the surrounding structures, responsibilities, and decision-making processes.
Agentic engineering is not a tooling topic.
It is a product and organizational topic.
What Comes Next
This article intentionally focused on mindset, principles, and practical observations — not on complete processes or templates.
In subsequent articles, I will go deeper into:
- how AI-led product discovery works
- how teams move from problem space to solution space
- which concrete artifacts emerge along the way
- and how roles evolve in agentic setups
Agentic engineering is a practice in the making.
All the more reason to talk about it clearly and precisely now.