Our Journey to Decuple Developer Productivity Using Agentic AI Coding and a Custom “Orchestration” Framework

Transforming Development Speed with AI-Driven Automation: Our Journey to 10x Efficiency

At our company, innovation isn’t just a buzzword—it’s a driving force behind our rapid growth. Recently, we have significantly accelerated our software development process, achieving what we like to call a tenfold increase in productivity. This breakthrough was made possible through the strategic integration of advanced AI coding assistants, specifically Claude Code, CodeRabbit, and a bespoke “Orchestration” layer designed to streamline workflows.

The core advantage lies in the collaborative nature of our AI agents. Unlike traditional automation tools, these AI entities don’t merely generate code—they actively review and critique each other’s work, fostering a self-improving development ecosystem.

Here’s a detailed overview of our innovative workflow:

1. Initiating Tasks: A new development task is logged in our project management system.
2. AI Task Retrieval: Our custom commands fetch and assign tasks to the AI agents.
3. Preliminary Study: The AIs analyze our existing codebase, project designs, documentation, and perform web research when necessary.
4. Task Definition: They craft comprehensive task descriptions, including specific test coverage requirements.
5. Code Implementation: The AI agents generate production-ready code adhering to our established standards.
6. Automated Pull Requests: A GitHub pull request is automatically created to integrate the new code.
7. Code Review: A second AI tool conducts an in-depth, line-by-line review of the proposed changes.
8. Feedback Loop: The first AI addresses the review comments, either accepting modifications or providing justifications.
9. Learning & Improvement: Both AI agents log their interactions, enabling continuous learning and refinement for future tasks.
10. Outcome: Typically, about 98% of the code is ready for deployment before any human intervention.

What’s truly fascinating is witnessing these AI agents “debate” implementation choices within GitHub comments. They’re effectively teaching each other, deepening their understanding of our code and becoming more adept developers over time.

For a closer look at this transformative process, we’ve prepared a brief 10-minute walkthrough video: Watch here.

While our current focus is on optimizing software development, we’re exploring ways to extend this system to other areas such as customer support and marketing. We’re eager to learn from others who are experimenting with AI-driven processes across different domains.

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