Boosting Developer Productivity by Tenfold Using Agentic AI Coding and a Custom “Orchestration” Layer
Maximizing Development Efficiency with AI-Driven Automation and Custom Orchestration
In today’s fast-paced digital landscape, accelerating product development without compromising quality is a challenge many teams face. Recently, our organization implemented an innovative approach that has transformed our workflow, enabling us to deliver advanced features at a remarkable pace. This strategy leverages advanced AI coding agents, combined with a tailored orchestration layer, to dramatically boost our development speed and accuracy.
A Key Innovation: AI-Powered Collaborative Coding
Our core breakthrough lies in deploying intelligent AI agents that not only generate code but also collaboratively review each other’s work. This self-sufficient review process ensures high-quality output with minimal human intervention, streamlining our release cycle.
Here’s a comprehensive overview of our process:
-
Task Initialization: Our project management system assigns tasks, which are then picked up by our AI agents through custom commands.
-
Research & Planning: The AI studies our existing codebase, references related documentation and designs, and performs web research as needed to inform its work.
-
Detailed Specification: It then crafts detailed task descriptions, including specific requirements for test coverage and performance standards.
-
Code Implementation: Using our coding guidelines, the AI writes production-ready code, careful to adhere to best practices.
-
Pull Request Generation: Once the code is ready, it automatically opens a pull request on GitHub.
-
Automated Peer Review: A second AI tool promptly reviews the proposed changes at a line-by-line level, providing detailed feedback.
-
Response & Refinement: The initial AI agent responds to this critique—either accepting suggestions or defending its implementation—thus forming a dynamic review loop.
-
Continuous Learning: Both AI agents learn from each interaction, refining their approaches for future tasks.
Remarkably, our workflow results in about 98% of code being production-ready before any human review—a testament to the power of this collaborative AI approach.
A fascinating aspect of this process is witnessing our AI agents “debate” implementation choices within GitHub comments. They effectively teach each other, continuously evolving their understanding of our codebase and best practices in real-time.
For a detailed walkthrough of this setup, we’ve recorded a short 10-minute video demonstrating how it all works: [https://www.youtube.com/watch?v=fV__0QBmN18](https://www.youtube.com/watch?v=fV__0QBm
Post Comment