What Is Vibe Coding? A 2025 Guide to Guardrails, Infrastructure & the Developer’s Role – Part 2
Guardrails, Infrastructure & the Developer’s New Role
Sharon Barr
Co-founder & CEO @ Early | Agentic AI Test Engineer
June 16, 2025
In 2025, a new paradigm of software development is emerging (and exploding!). Traditional coding is taking a back seat to conversational, prompt-first workflows. Known as vibe coding, this AI-powered approach lets developers describe what they want to an AI agent or chatbot, and get working code in return at unprecedented speed. But vibe coding comes with its own set of quality risks, which we believe will lead to vibe test coding exploding as well in the upcoming decade.
In this guide, we'll unpack what vibe coding means, how it works, when to use it, and how to overcome its quality challenges with vibe coding-driven testing. If you’re a developer, team lead, or group leader, or engineering manager, this article will help you understand the changes that are happening in development and how you can use AI in the upcoming decade to become a better developer.
But first, let’s start at the beginning, and look at the time code was born from cogs, vacuum tubes, and a few punch cards.
Table of contents:
AI in software development is evolving in record speed. In merely a few years since LLMs enabled coding at scale in 2022/3, 2025 is already the era of wide scale “vibe coding” for developers. Today, developers can describe their desired outcome in natural language prompts and get a working code in response. However, despite the advancements made in GenAI for software development, the code isn’t production-ready. It’s not even that close.
In this article, we’ll dive deep into the infrastructure and testing requirements that will allow developers, group leaders and engineering managers to use AI-generated code in production, driving scale and making developers better at their jobs.
Vibe coding capabilities are progressing fast. Here’s my vision for the expected advancements in the upcoming years:
For example, in 2024: Lovable added Google-Firebase login with five prompt lines for their authentication flow. In 2026, the same prompt, augmented with your internal API catalogue, could yield a bespoke AuthService class backed by your own OAuth cluster—no manual glue code required.
Vibe coding is advancing fast and the future looks bright. But to be able to really enjoy its advantages, we need code that can be trusted in production. This requires testing guardrails. Every slice of vibe-generated code must pass the same gauntlet before it can join the main branch:
- Near-perfect test generation - As soon as the feature appears, an agent synthesises a fresh battery of tests—unit, property-based, integration, even fuzz cases, so that the new logic is exercised from every angle and coverage, targeting a mutation score of 100%(!)
- Red-test discovery - Those fresh tests run. Any that fail light up red, exposing hidden bugs or mismatches between the prompt and reality.
- Self-healing repair cycle - The system dives in: analysing the failure, patching the code (or the test, if the expectation is wrong), re-running the suite, and keeping on iterating until the reds disappear.
- Regression sweep - With the new code now green, the entire legacy test suite is executed. If an old test breaks—because behaviour truly regressed or because a refactor invalidated an assertion—the agent first attempts an automatic repair: fix the bug, or update the test to reflect the legitimate change.
- Final green-lamp verification - A second, clean run of both the brand-new and the maintained legacy tests confirms everything still passes. Only then does the agent surface a merge request. Teams can insist on human approval at first; once the loop proves itself reliable, the gate can shift to auto-merge.
When achieved, All five steps will blur into an invisible guardrail woven directly into vibe coding: code arrives with its own test army, fresh reds reveal defects, self-healing eliminates them, legacy tests are updated or repaired, and a final all-green sweep certifies the build. Features will ship already proven safe—at AI speed—without sacrificing trust in what’s running in production. In other words, testing will become an automatic part of generating Production code via vibe coding, in just a few years.
Currently, studies already show test-LOC reaching, and sometimes topping, production-LOC in AI-heavy repositories. Looking ahead, we can expect 90–95% of all new lines to be tests, a ratio some teams are already hitting with automated unit-test generation today. Covered tests include:
- Functional tests (unit, component, integration E2E) tests ensuring the generated feature meets the natural-language acceptance criteria embedded in the prompt.
- Non-functional tests (Security, performance, localization, Usability)
- Regression scenarios replaying production traces against nightly builds.
- Self-healing suites where failing assertions trigger an agent to re-synthesize code or the test itself and propose a patch.
The Challenge: Technology and Infrastructure Haven’t Caught Up Yet
When Ford’s Model T first rattled down America’s dirt paths, the marvel wasn’t just the horseless carriage—it was the realization that the roads themselves weren’t ready. Ruts cracked wooden wheels, tires bogged in mud, and the earliest motorists often needed a passing rider to haul them free. Only after asphalt highways, service stations, and traffic laws emerged did mass-motoring become smooth and reliable.
Software is hitting its own Model T moment. AI tools can now stamp out mountains of application code and an even taller mountain of tests, but the underlying infrastructure—storage, CI runners, distributed test frameworks, smart caches, observability pipelines—must scale and harden to match that flood.
When it comes to testing, for example, test generators exist, but few reach near-total coverage without human guidance. Automated bug-fix loops succeed mostly on straightforward issues. Engineers still have to review diffs, tweak fragile legacy tests, and press Merge.
In the short-term, expect a period where cloud bills spike, test suites time-out, and flaky environments mimic those rutted roads. Then, as purpose-built artifact stores, ultra-parallel “test clouds,” self-healing CI/CD lines, and AI-optimized debuggers roll in, the path will smooth out. Once that digital highway is laid, vibe coding hits full throttle, teams ship and verify software at the speed of thought.
Paved roads followed the car, same with infrastructure to handle the immense amount of code generated.
The “digital highway” is a vision, but it’s rooted in tools that already exist in today’s GenAI software development ecosystem. For example:
- Authoring: Codex, Cursor, Loveable, all become agentic—they run isolated dev containers, iterate until tests pass, then raise a pull request with human-readable diffs.
- Quality: Emerging test agents that generate all forms of high coverage tests, self heal the new code from bugs and maintain existing tests that require refactoring
- CI/CD: Pipelines to monitor and execute test agents at scale
- Governance: Policy bots scan every PR for license, PII and AI-policy compliance, rejecting non-conformant contributions in seconds.
This upcoming future holds great potential for developers. While their role is changing, their value remains firm. Here’s what developers can expect during the era of “Vibe Coding”:
- From keyboard to captain’s chair - Your value moves from typing every semicolon to steering autonomous agents with crisp prompts, clear specs, and sound architectural vision.
- Quality is baked-in, not bolted-on - Tests are no longer a separate chore; they’re a first-class output of the generation loop. Your job is to frame the right assertions, spot quality gaps the agent missed, and approve the self-healing fixes—so velocity and confidence rise together.
- Code review evolves into curated verification - Review is no longer line-by-line nit-picking; it’s a higher-level quality- and-functionality gate, powered by its own AI tooling. You validate edge-case logic, security posture, performance budgets, and ethical constraints—deciding what ships, what refactors, and what never leaves the branch.
- Operator mindset. - Think SRE for code generation: monitor agent runs, dive into red tests, guide self-healing loops, and keep the release train humming.
- Velocity with confidence - Mastering these skills lets you deliver new functionality to production faster and safer than ever—turning AI super-power into customer value, not technical debt.
Closing Thoughts
Software development began with Ada Lovelace punching out Bernoulli numbers on hypothetical brass gears. In not much more than a century, we’re delegating entire production development pipelines to silicon collaborators that not only write the feature but also draft its proof of correctness and integration.
The shift from punch-cards to higher-level languages multiplied our power; the leap we’re taking now is bigger still. By conversing with agent-builders instead of crafting every line by hand, we’ll unlock hundreds or thousands of times more code, functionality, and delivered value in the same turn of the clock.
The real craft is no longer persuading machines with painstaking instructions; it’s learning to articulate intent so clearly that our silicon teammates can build the future at lightning speed—without sacrificing reliability.
If you’re interested in learning more about creating thousands of working, value add, LOCs at scale with GenAI, check out the blog “Generating 17,000 lines of working test code in less than an hour”.