Our Journey to Decuple Development Velocity Using Agentic AI Coding and a Personalized “Orchestration” Framework

Maximizing Development Efficiency with AI-Driven Automation and Custom Orchestration

In the fast-paced world of software development, staying ahead requires innovative approaches and cutting-edge tools. Recently, our team revolutionized our development process by integrating advanced AI coding agents alongside a bespoke “Orchestration” layer. This setup has exponentially increased our output, allowing us to deliver what would normally take months, now in a matter of weeks.

At the core of this transformation are AI agents that do more than just generate code—they actively review each other’s work, creating a dynamic, collaborative environment that accelerates quality and efficiency.

Here’s a breakdown of our streamlined workflow:

1. Initiation in Project Management Tool
   The process begins with task assignment and planning within our project management environment.

2. Task Retrieval and Preparation
   An AI agent fetches tasks through custom commands, examining our codebase, designs, and documentation. When necessary, it conducts web research to gather additional context.

3. Detailed Task Definition
   The AI then formulates comprehensive descriptions, including specific testing and coverage prerequisites.

4. Code Implementation
   Following our coding standards, the AI writes production-ready code tailored to the task requirements.

5. Automated Pull Request Creation
   Once completed, the AI automatically opens a pull request on GitHub, signaling the code is ready for review.

6. Peer Review by AI
   A second AI tool performs a meticulous line-by-line review of the submitted changes, acting as a reviewer.

7. Response to Feedback
   The original AI responds to review comments, either accepting suggestions or justifying its implementation choices.

8. Continuous Learning
   Both AI agents learn from each interaction, storing insights for future tasks to improve accuracy and efficiency.

The outcome? A remarkable 98% of code reaching a production-ready state before any human intervention.

What’s truly fascinating is witnessing how these AI agents debate implementation nuances directly within GitHub comments. They’re effectively teaching each other to become more proficient, constantly deepening their understanding of our unique codebase.

To illustrate this process, we’ve prepared a concise 10-minute walkthrough video detailing how the system operates: Watch here.

While our current focus is on development speed, we’re exploring broader applications of this automated orchestration—such as enhancing customer support or marketing workflows. We’re eager to hear from others experimenting with AI-driven system enhancements across different domains.

This era of intelligent automation is transforming how we build, review, and