Our Journey to Decuple Our Developer Productivity Using Agentic AI Coding and a Tailored “Orchestration” Layer

Transforming Development Efficiency with AI-Powered Orchestration at [Your Company Name]

In today’s fast-paced digital landscape, accelerating software delivery without compromising quality is a constant challenge. At [Your Company Name], we’ve pioneered an innovative approach that leverages advanced AI agents combined with a custom “Orchestration” layer to multiply our development speed tenfold.

Our methodology centers around using intelligent AI agents—tools like Claude Code, CodeRabbit, and others—that collaborate seamlessly throughout the development lifecycle. Unlike traditional automation, these agents actively review each other’s work, mimic peer code reviews, and evolve their skills through interaction, effectively creating a dynamic, self-improving development environment.

Here’s an overview of how our system operates:

1. Task Initiation: Our project manager captures a new feature or bug fix as a task.
2. AI-Driven Task Extraction: Custom commands enable AI agents to fetch and interpret these tasks.
3. Codebase Analysis & Research: The AI thoroughly examines our existing code, design guidelines, documentation, and performs web research where necessary.
4. Detailed Planning: It then formulates a comprehensive plan, including specific test coverage requirements.
5. Code Generation: The AI writes production-ready code aligned with our standards.
6. Automated Pull Requests: A GitHub pull request is generated automatically.
7. Peer Review via AI: A secondary AI agent reviews the changes line-by-line, providing feedback.
8. Iterative Improvement: The first AI responds—either accepting changes or defending its implementation—leading to an internal debate that enhances code quality.
9. Learning & Optimization: Both AI systems remember insights gained, continuously refining their approach for future tasks.

Remarkably, this process results in approximately 98% of code being production-ready before any human intervention. Watching these AI agents debate, critique, and learn from each other in real-time has been both fascinating and transformative—it’s akin to observing a team of expert developers refining their craft autonomously.

For a closer look, we documented a detailed 10-minute walkthrough showcasing this innovative workflow: Watch here

While our current focus is on development, we’re excited to explore applying this orchestration model to other domains such as customer support and marketing. We’re curious—what areas are you experimenting with AI automation in? Share your insights and experiences!

It’s an exhilarating time to be innovating in tech, and we look forward