Our Experience: Boosting Developer Efficiency Tenfold Using Agentic AI Coding and a Tailored “Orchestration” Layer

Transforming Software Development Efficiency with AI-Powered Orchestration

In today’s rapidly evolving tech landscape, maximizing development productivity is essential. At our organization, we’ve pioneered a groundbreaking approach that leverages advanced AI agents and a custom orchestration layer to accelerate our software delivery process exponentially.

By integrating tools such as Claude Code, CodeRabbit, and others into our workflow, we’ve achieved a remarkable tenfold increase in development speed. What’s truly transformative is how these AI agents collaborate: they aren’t just code generators but also critical reviewers—evaluating each other’s work to ensure high-quality output.

Here’s an overview of our innovative workflow:

1. Initiating Tasks: Our project manager assigns new features or fixes to the system.
2. Automated Task Retrieval: AI agents fetch and interpret these assignments through custom commands.
3. Comprehensive Analysis: The agents examine our existing codebase, design documents, and relevant web research to inform their work.
4. Detailed Planning: They craft precise task descriptions, including testing strategies and coverage requirements.
5. Code Development: The AI generates production-ready code aligned with our standards and best practices.
6. Pull Request Generation: A GitHub pull request is automatically created for review.
7. Dual Review Process: A second AI agent performs a meticulous line-by-line assessment of the proposed changes.
8. Developer Feedback Loop: The initial AI responds to review comments, either refining the implementation or providing justifications for its choices.
9. Continuous Learning: Both AI agents adapt based on interactions, progressively enhancing their performance for future tasks.

The outcome? We’re now delivering solutions that are approximately 98% ready for deployment before any human review, drastically cutting down manual QA cycles.

One of the most fascinating aspects is observing these AI entities engage in what feels like a technical debate—discussing implementation specifics directly within GitHub comments. It’s like watching a pair of developer minds training each other, gaining insights into our codebase along the way.

To illustrate this process, we’ve prepared a concise 10-minute walkthrough video that demonstrates the entire system in action: Watch here.

While our immediate focus has been on development, we’re exploring how to extend this methodology to other areas such as customer support and marketing. We’re eager to hear from others experimenting with similar systems—sharing ideas and insights to push the boundaries of AI-assisted workflows.

This is an exciting era for innovative builders, and integrating AI at