Our Journey to Decuple Developer Efficiency Using Agentic AI Coding and a Tailored “Orchestration” Framework

Revolutionizing Software Development: Achieving a 10x Increase in Velocity with AI-Driven Coding and Automated Orchestration

In today’s fast-paced tech landscape, staying ahead requires innovation not just in ideas but also in processes. Recently, our team embarked on a transformative journey, leveraging advanced AI tools and custom automation to drastically accelerate our development cycles. Here’s an inside look at how we are now delivering months’ worth of features every single week, thanks to an intelligent blend of AI coding agents and custom orchestration layers.

A Key Catalyst: AI Collaboration and Continuous Improvement

The core of our success lies in deploying AI agents that don’t merely generate code—they critically review each other’s work, fostering an environment akin to peer code reviews but far more efficient. This collaborative approach ensures higher quality, faster iterations, and a self-improving system.

Our Workflow in Detail

1. Task Initialization: Everything begins with our project management system where tasks are defined and prioritized.
2. AI Task Retrieval: Customized commands enable our AI agents to fetch and interpret tasks seamlessly.
3. Context Analysis: The AIs analyze our existing codebase, design documents, documentation, and, when needed, perform web research to gather additional insights.
4. Detailed Specification: They craft comprehensive task descriptions, including testing and coverage requirements.
5. Implementation: The AI agents develop production-ready code aligned with our coding standards.
6. Automated Pull Requests: Upon completion, a pull request is automatically opened on GitHub.
7. Peer Review by AI: A second AI agent reviews the new code meticulously, line by line.
8. Feedback Loop: The first AI agent responds to review comments, either accepting suggestions or defending its implementation.
9. Continuous Learning: Both agents learn from each interaction, continuously refining their capabilities for upcoming tasks.
10. Outcome: Our system produces code that’s approximately 98% ready for deployment before any human intervention is necessary.

What’s truly fascinating is observing these AI agents engage in simulated debates within GitHub comments, effectively teaching each other to become better developers. This self-educating process enhances understanding of our codebase and accelerates the development cycle.

Want to see this in action? We’ve prepared a concise, 10-minute walkthrough demonstrating exactly how this workflow functions: Watch the Video

Looking Ahead: Expanding Automation Beyond Development

While we’ve primarily focused on automating coding tasks, we’re eager to extend this orchestration approach