Boosting Our Development Pace Tenfold Using Agentic AI Coding and a Custom “Orchestration” Layer
Transforming Development Efficiency: Leveraging AI-Driven Coding and Custom Orchestration
In today’s fast-paced software landscape, accelerating development cycles without sacrificing quality is a constant challenge. Recently, our team pioneered a cutting-edge approach by integrating advanced AI coding agents with a bespoke orchestration system, resulting in a tenfold increase in our development speed.
Our journey centered around utilizing state-of-the-art AI tools such as Claude Code and CodeRabbit, which have fundamentally reshaped our workflow. The core innovation lies not just in the AI’s ability to generate code, but also in its capacity to collaboratively review and refine each other’s work, thereby elevating code quality and consistency.
Here’s a transparent look at our optimized process:
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Initiating Tasks: Every project begins with a clear task outlined in our project management system.
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Intelligent Retrieval: AI agents fetch tasks through custom command interfaces tailored to our workflow.
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Preliminary Analysis: They thoroughly examine our existing codebase, design documents, and relevant web research to understand context.
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Detailed Planning: The AI then formulates comprehensive task descriptions, explicitly defining testing and coverage criteria.
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Implementation: Following our coding standards, the AI writes production-ready code to address the specified requirements.
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Automated Pull Requests: The system automatically creates a GitHub pull request with the new code.
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Peer Review by AI: A secondary AI agent performs a meticulous line-by-line review of the proposed changes.
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Interactive Feedback: The first AI agent responds to review comments—either accepting suggestions or defending its choices.
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Continuous Learning: Both AI agents retain insights from each interaction, improving future performance and decision-making.
What results is remarkable: approximately 98% of code is production-ready before the human review stage.
What’s truly fascinating is witnessing these AI agents “debate” implementation strategies within GitHub comments, effectively teaching each other and becoming increasingly proficient at understanding our unique codebase.
For a detailed demonstration, check out our 10-minute walkthrough here: https://www.youtube.com/watch?v=fV__0QBmN18
While our current focus is on streamlining development, we’re also exploring how this systematic AI approach can be extended to other areas, particularly customer support and marketing. We’re eager to hear from others experimenting with similar strategies.
This is undoubtedly an exciting era for innovative building — the future of software development is here.
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