Our Journey to Decuple Development Speed Using Agentic AI Coding and a Tailored “Orchestration” Framework

Transforming Development Efficiency: How AI-Powered Orchestration Accelerates Our Workflow

In today’s fast-paced software landscape, accelerating development cycles without compromising quality is crucial. Recently, we’ve implemented a novel approach using advanced AI agents and a custom orchestration system that has dramatically increased our productivity—by a factor of ten. This post offers a behind-the-scenes overview of our innovative process, which leverages AI tools such as Claude Code and CodeRabbit to facilitate rapid feature delivery while maintaining high standards.

The core of our success lies in our AI-driven collaboration. Unlike traditional development, where tasks are handed off between developers, our AI agents actively review each other’s code, simulate peer programming, and engage in constructive debate—all within our version control environment. This dynamic exchange has become a powerful force multiplier, enabling us to generate highly reliable, production-ready code with minimal human oversight.

Here’s a step-by-step outline of our workflow:

1. Task Initiation: Requests start in our project management system.
2. AI Task Retrieval: Custom commands invoke AI agents to fetch and understand the task requirements.
3. Contextual Analysis: The AI reviews our existing codebase, design documents, documentation, and conducts web research as necessary.
4. Detailed Planning: The AI formulates a comprehensive task description, including specific test coverage criteria.
5. Implementation: It then develops production-grade code adhering to our coding standards.
6. Automated Pull Request Creation: A pull request is automatically generated on GitHub.
7. Code Review by AI: A second AI agent performs a meticulous, line-by-line review of the submitted code.
8. Feedback Loop: The initial AI responds to review comments—either accepting suggestions or defending its original approach.
9. Continuous Learning: Both AI agents record insights from each interaction, refining their understanding for future tasks.
10. Outcome: Remarkably, approximately 98% of code is production-ready before any human review is required.

One of the most fascinating aspects is observing how these AI agents engage in real-time debates within GitHub comments—essentially teaching each other and improving their coding proficiency through collaborative discussion. This emergent behavior is transforming the way we think about automated development.

To illustrate this process, we’ve prepared a concise 10-minute walkthrough video: Watch here.

While our current focus is on scaling development