Our Journey to a 10-Fold Increase in Development Speed Using Agentic AI Coding and a Tailored “Orchestration” Layer

Revolutionizing Development Speed: Leveraging AI Agents and Custom Orchestration for Rapid Innovation

In the fast-paced world of software development, staying ahead requires more than just traditional workflows. Recently, we achieved a tenfold increase in our development efficiency by integrating advanced AI coding agents, complemented by a bespoke orchestration layer. Here’s an inside look at how this innovative approach has transformed our process, enabling us to deliver months of features on a weekly basis.

Harnessing the Power of AI Collaboration

The cornerstone of our strategy involves deploying multiple AI agents that don’t merely generate code—they actively review and critique each other’s work. This peer-review mechanism ensures code quality is scrutinized thoroughly, greatly reducing the need for extensive human oversight.

Our Workflow in Action

1. Initiating Tasks: Every project begins with our project management system, where feature developments or fixes are logged.
2. Task Retrieval: Our AI agents extract assigned tasks through custom commands tailored to our workflow.
3. In-Depth Analysis: The AI agents analyze our existing codebase, technical documentation, and design specifications. When necessary, they conduct web research to fill in knowledge gaps.
4. Detailed Planning: Each agent crafts a comprehensive task description that includes specific testing requirements and success metrics.
5. Code Development: One of the AI tools generates polished, production-ready code adhering to our coding standards.
6. Pull Request Creation: The AI automatically opens a pull request within our version control system.
7. Automated Code Review: A second AI agent reviews the proposed changes line-by-line, providing feedback and suggestions.
8. Interactive Iteration: The initial AI responds to review comments, either defending its implementation or making necessary adjustments.
9. Knowledge Accumulation: Both AI agents learn from these interactions, continuously improving their understanding for future tasks.
10. High-Quality Output: This process results in code that is approximately 98% ready for deployment without human intervention.

An Unexpected Benefit: AI-Driven Code Quality Debate

One of the most fascinating aspects is witnessing the AI agents engage in constructive debates over implementation choices within GitHub comments. They effectively teach and learn from each other, becoming better developers as they familiarize themselves with our codebase—an almost teammate-like collaboration.

See it in Action

For those interested in how this works, we’ve recorded a concise 10-minute walkthrough demonstrating the entire process: [Watch the video here](https://www.youtube.com