I’ll be honest: when the idea was first proposed that we could shift all of our code production to AI, I wasn’t entirely convinced. Like many, I saw the potential in tools like Copilot and ChatGPT—but to imagine entire features, components, even products being built from end to end by AI agents? That seemed like a stretch.

And yet, here we are.

Since October 2024, we’ve been running Easylab AI on a fully AI-assisted development stack, where human engineers no longer write production code themselves. Everything we build is developed by agents, powered by LLMs like Claude 3.7 and DeepSeek GPT 4.1, orchestrated by prompt engineers, product designers, and dev strategists.

Let me walk you through how we got here—and what we’ve learned along the way.

It started with a first project: Linkeme. A SaaS we designed for SMEs, Linkeme helps automate and optimize their social media communications by combining audience insights, AI-driven scheduling, and generative content creation. We chose it because it was ambitious—but also self-contained. If it failed, no real damage. If it worked, it would prove the value of full-AI dev.

We gave it a spec, routed it through our early AI stack, and let the agents build. The first version ran… kind of. The logic was there, but fragile. Edge cases were missed, error handling was inconsistent, and the LLM misunderstood some of our intent. But despite the bugs, what struck us was the raw velocity. It took hours—not days—to get a first working version.

We iterated. Hard.

We started building internal tools to guide prompts, track agent output, inject context, and debug reasoning paths. We learned which models were good for what: Claude for logic and planning, DeepSeek for clean code generation, others for tests or documentation. We designed role-based agents, and built structured workflows around them. We enforced standards. We forced ourselves to slow down—just enough to design the orchestration layer properly.

There were failures. Some releases went sideways. There were moments where we thought, “Okay, maybe this was too much, too fast.” But each time, the lessons stuck—and the system got better.

Within weeks, we had restructured our delivery pipeline. Our engineers stopped writing components manually and instead began managing tasks as intentful orchestrators. They designed specs, prompted agents, reviewed code, validated logic, and shipped. They became system designers, reviewers, QA partners, and meta-programmers. And they got very good at it.

Today, we can say with confidence: our engineering team doesn’t just use AI—they build through AI. They operate on another level. They move faster, think bigger, and have developed a skillset that I believe will define the next decade of engineering: the ability to shape autonomous systems that build autonomously.

Is it perfect? No. We still hit edge cases. Prompting is an art, not a science. And we’ve had to invest a lot into internal tooling to keep the whole orchestration layer stable and explainable. But the benefits are undeniable.

What we’ve gained

  • We deliver features in 10–20% of the time it used to take
  • Our engineers are less fatigued and more intellectually engaged
  • We’ve built a scalable internal knowledge architecture through reusable agents
  • We’ve created a culture of systems thinking, experimentation, and speed

What’s still challenging

  • Prompting isn’t trivial. It requires training and intuition
  • LLMs hallucinate logic if instructions are vague
  • Multi-agent orchestration is fragile without the right sequence and fallback mechanisms
  • We had to build internal tools to track agent decisions and outputs

How it works in practice
  1. A spec is written in natural language
  2. bolt.new generates the base skeleton
  3. The “Back-end Builder” agent expands logic and integrates with data models
  4. Claude 3.7 refines the API logic and writes tests
  5. A “QA Validator” agent audits edge cases and coverage
  6. A human reviews, validates security, and deploys This entire process usually takes 1–2 days

We're still learning. Still improving. But today, I believe Easylab AI has one of the most forward-leaning dev cultures you’ll find in Europe. And it all started with a test project we were prepared to let fail.

If you’re considering a similar shift—or if you’ve already tried something like this—I’d love to hear your take. How far are you pushing AI in your own dev workflow? Where are the limits? Where are the surprises?

Let’s open the conversation.

Discover how Easylab AI builds with agents and LLMs

Check out Linkeme – our first fully AI-developed SaaS