At some point I realized we were building the same things over and over again. Not in a copy-paste way, but in a “we could generate 80% of this” kind of way. So last year, I ran a live-fire experiment: I asked Claude 3.5 and DeepSeek to build a small admin panel, with tests and API docs, from a plain-language spec.

The result: not great, but usable. It gave us the idea to stop typing code altogether.

Now, at Easylab AI, we don’t write code manually anymore. We use a stack of LLM-powered agents (Claude, DeepSeek, GPT-4) with structured task roles: • an orchestrator agent breaks down the spec • one agent builds back-end logic • another generates test coverage • another checks for security risks • another synthesizes OpenAPI docs • and humans only intervene for review & deployment

Agents talk via a shared context layer we built, and we introduced our own protocol (we call it MCP — Model Context Protocol) to define context flow and fallback behavior.

It’s not perfect. Agents hallucinate. Chaining multiple models can fail in weird ways. Debugging LLM logic isn’t always fun. But…

We’re faster. We ship more. Our team spends more time on logic and less on syntax. And the devs? They’re still here — but they’ve become prompt architects, QA strategists, and AI trainers.

We built Linkeme.ai entirely this way — an AI SaaS for generating social media content for SMEs. It would’ve taken us 3 months before. It took 3 weeks.

Happy to share more details if anyone’s curious. AMA.