How We Boosted Our Developer Productivity by Ten Times Using Agentic AI Coding and a Customized “Orchestration” Layer

Accelerating Development Efficiency with AI-Powered Coding: Our Experience with Agentic AI and Custom Orchestration

In today’s fast-paced tech landscape, rapid deployment of features can be the key to staying ahead. Recently, our team embarked on an innovative journey to enhance our development process by integrating advanced AI tools and a bespoke “Orchestration” layer. The results have been transformative, enabling us to deliver what previously took months in just a matter of weeks.

At the heart of this breakthrough is the strategic deployment of AI agents that do far more than generate code—they critically evaluate each other’s work to ensure quality and adherence to standards. This dynamic interaction accelerates development cycles while maintaining high standards.

Here’s an overview of our streamlined workflow:

1. Project Initialization: Our project manager initiates tasks within the system.
2. AI Task Acquisition: Custom commands fetch tasks and relevant information.
3. Contextual Study: AI agents analyze our codebase, design documents, documentation, and perform web research if necessary.
4. Task Specification: A comprehensive description is generated, including testing requirements and coverage expectations.
5. Code Implementation: An AI agent develops production-ready code in line with our development guidelines.
6. Pull Request Generation: The AI automatically opens a GitHub pull request for review.
7. Code Review: A second AI agent performs a meticulous line-by-line review of the proposed changes.
8. Feedback Loop: The first AI responds to review comments—either defending the approach or making necessary adjustments.
9. Continuous Learning: Both AI agents learn from each interaction, enhancing their future performance.
10. High-Quality Output: This process results in approximately 98% of code being production-ready before any human intervention.

What’s truly fascinating is observing these AI agents “debate” implementation details within GitHub comments. They effectively learn from each other, becoming increasingly adept at understanding our codebase and best practices—almost like a collaborative development team.

For those interested, we’ve documented this process in a short 10-minute walkthrough video: https://www.youtube.com/watch?v=fV__0QBmN18

Beyond development, we’re exploring how this method can extend into other areas such as customer support and marketing. We’re eager to hear about initiatives others are pursuing in these fields or insights into similar system-driven approaches.

The potential of AI to revolutionize how we build and