In an era where AI-assisted development is becoming the norm, one company has taken the leap further than most. Since October 2024, Easylab AI, a Luxembourg-based firm specializing in applied artificial intelligence, has ceased writing production code manually altogether. Instead, the team relies entirely on a system of AI agents powered by large language models like Claude 3.7 and DeepSeek GPT 4.1 to build, test, and iterate on software projects—end to end.
The decision wasn’t taken lightly. Easylab AI develops intelligent systems for clients in sectors like logistics, customer service, and financial services. Reliability, security, and development velocity are all critical. But according to the company, the shift away from manual coding has not only improved output quality but also allowed engineers to focus on higher-level tasks such as architecture, validation, and system orchestration.
“We realized that our bottleneck wasn’t technical anymore—it was cognitive. There was too much low-value work being done manually,” explains a lead engineer at Easylab AI. “We had access to extremely capable models, but we weren’t using them to their full potential.”
The company’s current development workflow is based on a stack that includes bolt.new for initial codebase generation, Cline, a VS Code-integrated assistant for structured dialog with AI agents, and a proprietary agent orchestration layer. Rather than relying on a single model, Easylab AI uses multiple LLMs depending on the task. Claude 3.7 is preferred for reasoning-heavy or planning-oriented work, while DeepSeek GPT 4.1 is used for clean, modular code generation at scale.
What makes the setup unique is the company’s use of role-based agents. Tasks are distributed to agents with specialized responsibilities—API architects, backend builders, QA validators, and even security reviewers. These agents operate independently or in sequence, producing and refining components before a human orchestrator steps in for final validation.
This isn’t about removing engineers, the team insists. It’s about redefining the role of a software engineer as an orchestrator rather than a line-by-line coder. Engineers at Easylab AI now spend their time writing specifications, crafting structured prompts, debugging agent logic, and guiding workflows—what the company refers to as “building the builders.”
The results are notable. Internal tools and MVPs are delivered in a fraction of the time it used to take. Codebases are more consistent. And engineers report increased engagement, as they can focus more on problem-solving than boilerplate.
The company shared a typical use case: when building a reporting module, the process starts with a natural language spec. bolt.new generates a base skeleton. A backend agent writes business logic and integrates data models. Claude 3.7 refines the API layer and produces test cases. A QA validator agent checks for edge cases. Finally, a human reviews and deploys. This entire process typically takes one to two days.
What they’ve gained:
• 10x faster delivery of internal tools and MVPs
• Consistent, modular code structure
• Focus shifted from syntax to architecture
• Scalable workflows through agent reuse
What remains challenging:
• Prompt engineering still requires expertise and iteration
• Poorly scoped instructions lead to hallucinations or logic errors
• Orchestrating multiple agents requires careful sequencing
• The company had to build internal tools to trace agent decisions
As AI tooling continues to mature, Easylab AI sees its model as an early but scalable version of what may become standard practice across the software industry. “We’re not saying this works everywhere,” the team notes, “but we’re convinced that orchestration-first engineering is the future of software development.”
More on their approach can be found at www.easylab.ai.
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