Achieving a Tenfold Increase in Development Speed Using Agentic AI Coding and Our Customized “Orchestration” Layer

Accelerating Development Efficiency with AI-Orchestrated Coding: A Case Study in Innovation

In today’s fast-paced digital landscape, maximizing development velocity is crucial. Recently, our team achieved a remarkable tenfold increase in our software delivery pace by integrating advanced AI agents and a tailored orchestration framework into our workflow. Here’s an inside look at how leveraging tools like Claude Code, CodeRabbit, and custom processes transformed our development pipeline, enabling us to deliver features at an unprecedented rate.

The core driver of this efficiency surge is the collaborative nature of our AI agents. Unlike traditional automation, these AI collaborators don’t merely generate code—they evaluate and critique each other’s work in real-time, fostering a self-improving environment.

Our streamlined workflow unfolds as follows:

1. Initiation: The process begins with a task assigned within our project management system.
2. Task Retrieval: An AI agent fetches the task via specialized commands.
3. Codebase Analysis: The AI reviews our existing code, designs, documentation, and conducts supplementary web research as needed.
4. Task Breakdown: It formulates a comprehensive task description, complete with testing criteria and coverage expectations.
5. Implementation: The AI writes production-ready code aligned with our coding standards and best practices.
6. Pull Request Automation: The system automatically opens a GitHub pull request for review.
7. Automated Code Review: A second AI tool conducts a meticulous line-by-line review of the proposed changes.
8. Feedback Loop: The first AI responds to the reviewer’s comments, either accepting suggestions or defending its original approach.
9. Continuous Learning: Both AI agents document their interactions and lessons learned, enhancing their performance on future tasks.
10. Outcome: We achieve approximately 98% deployment-ready code prior to human review, significantly reducing manual revisions.

One of the most fascinating aspects of this process is observing how the AI agents engage in a debate over implementation details directly within GitHub comments. This dynamic interaction resembles a collaborative learning environment, where each AI refines its understanding of our codebase and development standards—almost as if they’re mentoring each other.

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

Looking ahead, we’re exploring ways to extend this AI orchestration approach beyond development—considering applications in customer support and other areas of our operations. We’re also keen to learn from others