Our Journey to Decuple Our Development Speed Using Agentic AI Coding and a Custom “Orchestration” Layer

Transforming Development Efficiency with AI-Driven Automation: Our Journey to 10x Speed Using Agentic AI and Custom Orchestration

In today’s fast-paced tech landscape, accelerating development cycles without compromising quality is a persistent challenge. Recently, our team achieved a remarkable breakthrough by integrating advanced AI agents into our workflow, enabling us to multiply our productivity tenfold. Here’s an inside look at how we harnessed tools like Claude Code, CodeRabbit, and our proprietary “Orchestration” layer to revolutionize our coding process.

The cornerstone of this transformation lies in the collaborative efforts of AI agents that don’t merely generate code—they actively review each other’s work. This peer-review mechanism ensures high-quality output with minimal human intervention, allowing us to deliver features at a pace previously thought unattainable.

Our streamlined workflow unfolds as follows:

1. Initiation from the project management interface, where task details are entered.
2. Our AI system retrieves tasks through customized commands tailored to our processes.
3. The AI studies our existing codebase, designs, documentation, and conducts web research when necessary.
4. It then generates a comprehensive task brief, explicitly detailing testing and coverage expectations.
5. Next, the AI writes production-ready code in strict adherence to our coding standards.
6. A GitHub pull request is automatically created to facilitate review.
7. A second AI agent performs a meticulous, line-by-line review of the submitted code.
8. The first AI agent responds to any feedback—either accepting the suggestions or providing justifications for its original implementation.
9. Both AI agents learn from each interaction, storing insights to enhance future tasks.
10. Consequently, approximately 98% of the code reaches a deployment-ready state before any human review.

What truly captivates us is witnessing these AI agents engage in development debates within GitHub comments. They effectively teach each other and deepen their understanding of our codebase, functioning as continuous, self-improving developer counterparts.

To illustrate this process, we recorded a concise 10-minute demonstration that walks through the entire system in action. You can watch it here: https://www.youtube.com/watch?v=fV__0QBmN18

While our primary focus has been on refining development, we’re keen to extend this methodology to other domains such as customer support and marketing. We believe this systemic approach holds transformative potential across various organizational functions.

We’re living in an exciting era of