AI-first engineering
Google's engineers just described the new SDLC. We were running it before it had a name.
SDLC / software development life cycle / noun : the end-to-end process behind every piece of software: planning it, building it, testing it, shipping it, and maintaining it
The whitepaper: published by Google, written by Addy Osmani, Shubham Saboo, and Sokratis Kartakis, May 2026. Read it on Kaggle.
The paper's claim: software engineering is going through its biggest transformation since high-level programming languages. AI handles implementation; humans provide intent, architecture, and judgment.
That division of labor is not a prediction to us. It's our production line, and the reason we can sell all the software you need for $4,500 a month, flat.
From the paper
- "As of early 2026, 85% of professional developers regularly use AI Coding Agents, 51% use them daily, and an estimated 41% of all new code is AI-generated."
- "Fully autonomous agents can clone repositories, plan multi-file changes, execute them in sandboxed environments, run tests, and submit pull requests — all without a human typing a single line of code."
Google's New SDLC whitepaper, May 2026
What the paper actually says
Not hype about prompts. It describes a new division of labor, and three claims carry the whole argument.
The machine implements
"A new paradigm has arrived in which developers express what they want to build rather than how to build it. The machine handles implementation. The human provides intent, architecture, and judgment."
Discipline is the differentiator
"The key differentiator is not whether you use AI. It's how much structure, verification, and human judgment surrounds the AI's output."
The output is a factory
"The developer's primary output is not code — it's the system that produces code."
All quotes from Google's New SDLC whitepaper (May 2026), quoted here for commentary. Vroni is not affiliated with, or endorsed by, Google or the authors.
From ad-hoc prompting to agentic engineering
The disciplined end of the spectrum is where we live.
The paper's most useful idea is a spectrum; its own subtitle runs "from ad-hoc prompting to agentic engineering." At one end sit casual prompts, "does it seem to work?" verification, and code nobody reads. Fine for prototypes, risky for anything real. At the other end, what it calls agentic engineering: formal specs, memory, automated test suites and CI gates, thorough review. "AI handles implementation details," humans handle the architecture. Low risk, built for production systems.
We built Vroni to live at the far end of that spectrum, and we call our practice AI-first engineering: clear rules for what the AI does and what our human senior engineers do. The AI produces; seniors set intent and architecture, make the decisions, verify correctness, and own the result.
Their checklist, our production line
- 01"Formal specs, architecture docs, memory files": Vroni keeps a per-repository memory of your conventions, architecture, and past decisions. It never starts from a blank slate.
- 02"Automated test suites, CI/CD gates": every run plans, implements, executes checks, and repairs failures before anything is handed over.
- 03"Humans handle architectural issues": our human senior engineers make the technical calls and review every line before it reaches your repository.
- 04"Systematic verification at every stage": that's the whole point of building the factory instead of typing the code.
The 80% problem is why our humans are senior
The paper names the trap most AI-built software falls into. Without meaning to, it also explains our hiring policy.
"AI agents can rapidly generate approximately 80% of the code for a feature, but the remaining 20% — the edge cases, error handling, integration points, and subtle correctness requirements — demands deep contextual knowledge that current models often lack. [...] These errors are harder to detect precisely because the code 'looks right' and may even pass basic tests."
Google's New SDLC whitepaper, May 2026
Code that looks right, passes the tests, and is still wrong is exactly what you get when inexperience meets AI speed. The 20% is where products break, and spotting it is a judgment skill that takes years to build. So we made a simple rule: the AI does the 80% at machine speed, and only senior engineers touch the 20% that decides everything.
Mixed teams, the old way
Seniors and juniors typing everything by hand. Senior judgment only where seniors have time; the rest learns on your budget. Solid, slow, and priced like headcount, because it is headcount.
Juniors, ad-hoc prompting
Fast and cheap until the 20% bites: wrong assumptions about business logic, missing edge cases, architecture that quietly rots. The code looks right and passes basic tests (the paper's words) right up to production.
AI-first engineering: the flat rate
The AI does the production work. Our human senior engineers, and only seniors, set the architecture, make the decisions, and verify the 20% that matters. Better than the old way, faster than both, at a fraction of the price.
The factory model
They describe a factory. We already built one.
The paper's mental model for the new SDLC: "A factory manager does not assemble every widget by hand. They design the assembly line and ensure quality control." Specifications and context in; agents that translate them into implementation; tests and quality gates; feedback loops that route failures back for correction; guardrails around all of it.
That is a description of Vroni. We built the assembly line before it had a whitepaper, and today it ships 100+ software tasks to production every month, with our human senior engineers running quality control. The flat rate is that factory, sold as an outcome: you send what you have, and finished pull requests land in your repository.
The new SDLC, as a service
- 01Send whatever needs building, by email or GitHub. Rough notes are fine.
- 02The factory produces; our human senior engineers architect, decide, and verify.
- 03Reviewed pull requests land in your repository, follow-ups included.
- 04$4,500 a month, flat, one task at a time. Cancel anytime; the first month carries a 14-day money-back guarantee.
The new SDLC is here. You don't have to build it.
Send us one real task and see the factory run: a finished, senior-reviewed pull request in your repository, with a 14-day money-back guarantee covering your first month.