Our Journey to Decuple Developer Efficiency Using Agentic AI Coding and a Personalized “Orchestration” Framework

Accelerating Development Efficiency with AI-Driven Automation and Custom Orchestration

In today’s rapidly evolving tech landscape, increasing development velocity is paramount. Our recent implementation of AI-powered coding agents combined with a bespoke orchestration layer has revolutionized our software delivery process, enabling us to ship months’ worth of features on a weekly basis.

How We Achieved a Tenfold Improvement in Developer Productivity

At the core of this transformation are advanced AI agents—specifically, language models integrated with our workflows—that don’t just generate code but actively review and critique each other’s work. This peer-review mechanism accelerates quality assurance and significantly reduces manual oversight.

Our Workflow in Detail

1. Task Initialization: A project manager formulates and assigns tasks within our system.
2. Automated Task Retrieval: Custom commands enable AI agents to fetch new tasks seamlessly.
3. Deep Code and Documentation Analysis: AI reviews our existing codebase, design docs, and relevant web resources to understand context.
4. Detailed Task Specification: The AI crafts comprehensive task descriptions, including defining testing and coverage criteria.
5. Code Implementation: The AI generates production-quality code aligned with our standard guidelines.
6. Automated Pull Request Creation: A GitHub pull request is automatically generated by the AI.
7. Peer Code Review: A second AI agent reviews the proposed changes line-by-line, providing feedback.
8. Response and Adjustment: The initial AI either accepts the suggested modifications or defends its approach, prompting further refinement.
9. Continuous Learning: Both AI agents log their interactions, enhancing their understanding for future tasks.

Remarkably, our system achieves approximately 98% of code being production-ready prior to human review, drastically reducing manual review time.

A Dynamic and Self-Improving Codebase

One of the most fascinating aspects of this approach is witnessing AI agents engaging in ‘debates’ over implementation details within GitHub comments. This emergent interaction fosters a form of collaborative learning, where the AIs effectively teach one another, continuously refining their development skills and understanding of our application.

For those interested, we’ve prepared a concise 10-minute walkthrough illustrating this entire process here.

Looking Ahead

While we’ve focused on streamlining development, we see tremendous potential in applying this orchestration methodology to other areas—such as customer support or marketing—to further amplify operational efficiency. We’re eager to