Boosting Our Development Velocity by Tenfold Using Agentic AI Coding and a Tailored “Orchestration” Framework

Revolutionizing Development Efficiency: How Agentic AI and Custom Orchestration Transformed Our Workflow

In the rapidly evolving landscape of software development, staying ahead requires innovative approaches and cutting-edge tools. Recently, our team embarked on a journey to significantly boost our development velocity by integrating advanced AI agents alongside a bespoke “Orchestration” layer. The results have been nothing short of transformative, allowing us to deliver the cumulative equivalent of months’ worth of features each week.

At the core of this breakthrough is the deployment of intelligent AI agents that not only generate code but also engage in peer review, fostering a collaborative environment that mimics a real-world development team. This synergy accelerates the entire process, ensuring high-quality output with minimal human intervention.

Here’s an overview of our streamlined workflow:

1. Initiation: A new task enters our project management system.
2. Task Retrieval: AI agents extract and interpret the task through custom command interfaces.
3. Contextual Analysis: They examine our codebase, design documents, and documentation — augmented with web research if necessary.
4. Planning: The AI formulates a detailed task description, including testing and coverage requirements.
5. Implementation: They develop production-ready code aligned with our coding standards.
6. Integration: An automatic pull request (PR) is created on GitHub.
7. Peer Review: A second AI agent performs a meticulous line-by-line review of the proposed changes.
8. Feedback Loop: The primary AI reviews the feedback, defending or refining its implementation accordingly.
9. Continuous Learning: Both agents store insights from each engagement, continuously improving future performance.

Remarkably, this process results in around 98% of the code being production-ready before any human review takes place. Watching these AI agents collaborate and debate implementation strategies directly within GitHub comments is a fascinating experience — akin to two seasoned developers honing their craft through iterative dialogue. It’s a system where AI effectively teaches itself to write better, more reliable code over time.

For those interested in seeing this process in action, we’ve recorded a comprehensive 10-minute walkthrough available here: https://www.youtube.com/watch?v=fV__0QBmN18.

While our current focus is on development, we believe this systemic approach holds potential for other domains, such as customer support and marketing. We’re eager to explore these possibilities and would love to hear from anyone experimenting with similar innovations.

Stay tuned as we