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

Accelerating Development Efficiency by Tenfold with AI-Driven Coding and Customized Orchestration

In today’s rapidly evolving tech landscape, streamlining the software development process is more critical than ever. Our team has recently harnessed the power of advanced AI tools combined with a bespoke orchestration layer to significantly boost our productivity. Here’s an insider’s look at how we are delivering months’ worth of features on a weekly basis by leveraging cutting-edge AI technologies like Claude Code and CodeRabbit.

The core innovation lies in deploying AI agents that not only generate code but also participate in peer review—effectively creating an autonomous, collaborative development environment. This approach has revolutionized our workflow, leading to faster, higher-quality outputs with minimal human intervention.

Our Development Workflow:

1. Initiation: A new task originates within our project management system.
2. Task Retrieval: An AI agent fetches the task through custom command integrations.
3. Analysis: The AI examines our existing codebase, design documents, and relevant web research to gather context.
4. Specification: It constructs a comprehensive task description, including specific requirements for test coverage.
5. Implementation: The AI crafts production-ready code aligned with our internal standards and best practices.
6. Integration: A GitHub pull request is automatically generated upon code completion.
7. Peer Review: A second AI agent performs an immediate, detailed line-by-line review of the proposed changes.
8. Feedback Loop: The initial AI responds to review comments—either accepting suggestions or providing defenses for its original approach.
9. Continuous Learning: Both AI agents log learnings from each interaction, refining their future performance.
10. Outcome: Approximately 98% of the code reaches production readiness before involving a human reviewer.

One of the most remarkable aspects of this system is observing the AI agents “debate” implementation strategies directly within GitHub comments. This process fosters an iterative learning cycle where each agent enhances its understanding of our codebase and development standards, effectively teaching itself to become a more proficient developer.

To provide a clearer picture, we’ve recorded a concise 10-minute walkthrough demonstrating these capabilities in action. You can view it here: Watch on YouTube.

While our initial focus has been on accelerating development, we are eager to explore expanding this orchestration approach into other domains such as customer support and marketing. If you’re experimenting with AI-driven systems in your operations, we’d love to hear about your experiences and