How Vibe Coding Rewrites the SDLC, a Developer’s Field Notes

The software industry is undergoing a profound transformation, driven by advances in AI. Alexey Astakhov, Vice President of Engineering at *instinctools, shares his insights on how vibe coding is reshaping the developer’s role, redefining the process of software creation, and empowering teams to turn ideas into working prototypes.

If you’ve been in software engineering long enough, you start to notice the same cycles repeat – new frameworks, new methodologies, and the same relentless pressure to deliver faster and cheaper. The software development lifecycle has gone through countless transformations – from Agile to CI/CD – each promising greater efficiency. But now and then, something comes along that doesn’t just make us more efficient – it changes the rules entirely.

Currently, that something is vibe coding: developing software by orchestrating AI agents through natural language, rather than manually writing every line of code. It’s more than a productivity boost. It’s a new way of thinking – one that doesn’t just optimise the system, but redefines how the entire process works.

From Idea to MVP in Record Time

Vibe coding matters because it helps teams move from an idea to an MVP – and eventually to a full-scale solution – significantly faster.

AI agents can now take over a substantial part of the coding workload, trimming 20–30% off development time on average. For well-scoped tasks, we’re seeing nearly double the productivity. That means what once took a team of developers a quarter to deliver can now be achieved in a matter of weeks – sometimes even days – with one experienced engineer coordinating a number of specialized  AI agents.

At *instinctools, we’ve seen this play out firsthand. There are projects where a single engineer supported by AI agents creates a functional MVP in a couple of weeks instead of the three-month work of a 3-4 people’s team.

But speed isn’t the whole story. The real advantage is agility. When you can prototype multiple ideas in parallel, the cost of failure drops dramatically – and the pace of learning skyrockets. You’re no longer betting months of work to prove one single idea. Instead, you’re running a series of quick, data-driven experiments that show you what actually works better.

The Developer as a Conductor

One of the biggest misconceptions about vibe coding is that it replaces developers. In reality, it changes what developers do.

Instead of writing every line of code, developers now set the scene by defining goals, establishing quality standards, and shaping the project’s context, allowing AI agents to work effectively. It’s less about typing and more about conducting – leading a team of capable, if occasionally unpredictable, digital assistants.

That shift demands a higher level of seniority and broader thinking. Developers need to understand architecture, testing, and security, but they also need the soft skill of explaining ideas clearly – because now, they’re doing it to both humans and AI.

What really sets vibe coding apart is the way it reimagines the developer’s role – shifting from writing code in a programming language to orchestrating it through natural language.

How Vibe Coding Differs From Traditional Development

In traditional software development, engineers still use languages like Python, Java, or C++, but their work is often less about writing code from scratch and more about adapting and refining what already exists. It’s careful, precise work,  but it can also be repetitive and time-consuming.

Vibe coding redefines that process entirely. Instead of manually writing code, developers communicate with AI agents in natural language, describing what needs to be built, refined, or tested, and the system generates the corresponding code automatically.

This shift does more than just save time. It changes how teams think about building software. Routine, repetitive tasks that once consumed hours – writing boilerplate, creating tests, or refactoring code – now take minutes. Developers can focus on higher-level design, architecture, and problem-solving, while AI handles the heavy lifting in the background.

What Vibe Coding Does Best

Certain tasks are a natural fit for vibe coding. Prototyping and proofs of concept (PoCs) are at the top of that list – as are writing tests, converting code between programming languages, and automating documentation.

The key is clarity. The more structured your inputs and expectations are, the better the AI performs. It’s like having a brilliant assistant – but one who needs clear instructions and boundaries to do their best work.

Done right, vibe coding turns what used to be tedious, manual work into a dynamic exchange of ideas – one where developers can focus on design and decision-making instead of boilerplate code.

Understanding and Mitigating the Risks

Like any powerful tool, vibe coding comes with risks – and it’s essential to manage them deliberately. Over the past few years, we’ve learned that several areas need the most attention.

1. Security

One of the biggest mistakes is experimenting with public AI models without proper safeguards. It might seem harmless, but even a single exposed access key or data snippet can create real vulnerabilities.

Isolate your agents in sandboxed environments, restrict their permissions, and establish clear data-access policies. Regular audits and internal reviews should become a routine part of the workflow.

2. Loss of Control

Without proper validation, it’s easy for an AI-driven pipeline to drift from the intended architecture or quality standards.

Introduce systematic validation steps at each development milestone (PoC → MVP → production), supported by regression checks and external audits.

3. Human Stagnation

The danger isn’t that AI will replace developers. It’s that developers will stop growing because AI feels easier. Delegation without reflection leads to skill atrophy.

Build continuous learning into the process. Encourage developers to retrain, reflect on AI decisions, and stay actively involved in architectural and design choices.

4. Code Maintainability

AI-generated code can be inconsistent or hard to read, especially when multiple agents contribute.

Make post-task assessments part of the delivery pipeline. Automated linters, formatters, and style enforcement tools (like Prettier or ESLint) should run by default to keep code clean and standardised.

With these guardrails in place, vibe coding becomes not only safe but often safer than traditional coding, because you’re building security and quality into the process from day one.

How to Start Your Vibe Coding Journey

If your team is new to vibe coding, the best advice is simple: just start. Don’t wait for the perfect setup – pick a use case, choose a tool, and start experimenting. The market for AI coding tools is growing fast, and the best way to learn is by using them on real tasks.

Once you’ve selected a tool or platform, the next stage is to build a supporting infrastructure layer around it. This company-wide “middleware” should handle essential checks for testing, security, and efficiency.

After that, it’s time for agent onboarding. Define the boundaries, share your coding conventions, and provide sufficient context for your AI agents to make informed decisions. With this foundation in place, most teams can reach a stable, repeatable workflow within four to eight weeks – and deliver a production-grade MVP in the same timeframe.

The Future of Software Development

Not long ago, building software required a whole team of engineers. Today, that team often includes a network of AI agents working alongside humans. The real advantage now belongs to developers who can orchestrate these agents – guiding them across the entire lifecycle, anticipating risks, and steering projects with a mix of technical skill and human intuition.

Vibe coding isn’t just another tool in a developer’s stack. It’s a new way of working – one that blends human creativity with machine precision, turning the SDLC into something far more flexible, collaborative, and ever-evolving process.