Our Journey to Decuple Our Development Velocity Using Agentic AI Programming and a Tailored “Orchestration” Framework

Transforming Development Workflow with AI-Powered Orchestration: A Case Study

In today’s fast-paced digital environment, accelerating software delivery without compromising quality is a game-changer. At our organization, we’ve achieved a tenfold increase in development speed by harnessing advanced AI tools combined with a meticulously designed orchestration layer. This innovative approach enables us to deploy features at a pace previously thought unattainable, all while maintaining high standards of code quality.

Our strategy revolves around the intelligent collaboration of AI agents that not only generate code but also review and refine each other’s work. This self-improving mechanism drastically reduces the need for manual oversight, enabling us to achieve up to 98% production-ready code before human intervention.

Here’s an overview of our streamlined workflow:

1. Task Initialization: Our project management system kicks off the process by defining the task.
2. Task Retrieval: An AI agent fetches the task using custom commands tailored to our environment.
3. Contextual Review: The AI studies our existing codebase, designs, documentation, and performs web research as needed.
4. Detailed Specification: It then constructs a comprehensive task description, specifying test coverage and implementation details.
5. Coding Phase: The AI develops production-ready code aligned with our coding standards.
6. Automated Pull Request: A GitHub pull request is automatically generated.
7. Peer Review by AI: A secondary AI agent reviews the code line-by-line, providing feedback.
8. Interactive Refinement: The first AI responds to the review, either accepting suggestions or defending its implementation.
9. Learning Loop: Both AI agents learn from each interaction, continually refining their processes.
10. Deployment Readiness: The outcome is highly matured code, requiring minimal human review.

What’s truly fascinating is observing the AI agents engage in dynamic conversations through GitHub comments, effectively teaching and enhancing each other’s capabilities as they gain a deeper understanding of our codebase.

To illustrate this process, we’ve prepared a concise 10-minute walkthrough video that explains our system in detail: Watch it here.

While our current focus is on coding automation, we’re exploring ways to extend this orchestration approach into other areas such as customer support and marketing. We’re eager to hear from others experimenting with similar systems—especially in how AI can revolutionize team workflows and operational efficiency.

Innovations like this position us at the forefront of modern software development, making this an