Building in Public with AI Agents

Most companies treat their development process as a black box. The client sends requirements, waits weeks or months, and receives a product. What happens inside -- the decisions, the trade-offs, the failures, the pivots -- is hidden behind NDAs and project management dashboards.

We chose a different path. We build in public because we believe transparency creates trust that marketing never can. When a potential client can see how we work -- the agent orchestration, the testing protocols, the quality standards -- they do not need to take our word for it. They can evaluate it themselves.

Building in public with AI agents is also a forcing function for quality. When your process is visible, you cannot cut corners. Every architectural decision, every testing shortcut, every quality trade-off is on display. This pressure is uncomfortable but valuable. It keeps us honest about what our system can and cannot do.

The most interesting side effect of building in public is the community it creates. Other founders, developers, and AI enthusiasts follow our process, offer feedback, and share their own experiments. This creates a knowledge network that benefits everyone, including us. We learn from our community as much as they learn from us.

There is a strategic dimension too. In a market full of AI claims and vaporware, demonstrating real work is the most powerful differentiator. Anyone can write a pitch deck claiming AI-powered development. Very few can show the actual agent logs, test results, and deployment pipeline. We can.