Our Journey to Decuple Development Velocity Using Agentic AI Coding and a Custom “Orchestration” Layer

Revolutionizing Development Speed: Leveraging AI Agents with Custom Orchestration at Our Company

In today’s fast-paced tech landscape, accelerating development cycles is key to staying ahead. At our organization, we’ve harnessed the power of AI-driven coding solutions combined with a bespoke orchestration system to exponentially boost our productivity. Here’s an inside look at how we achieve rapid feature deployment—sometimes multiple months’ worth in just a week.

Our approach revolves around autonomous AI agents that not only generate code but also critically evaluate each other’s work. This collaborative AI process ensures high-quality, production-ready code with minimal human intervention.

Here’s a breakdown of our streamlined workflow:

1. Initiation: Tasks commence within our project management platform.
2. Task Retrieval: AI agents access assigned tasks through custom command integrations.
3. Research & Planning: They analyze our existing codebase, design documents, and relevant online resources to inform their work.
4. Specification: Detailed task descriptions are formulated, including requirements for testing coverage.
5. Development: AI agents implement robust, production-ready code adhering to our coding standards.
6. Automation: A pull request is automatically generated on GitHub.
7. Peer Review: A secondary AI agent performs a meticulous line-by-line review of the proposed code.
8. Feedback Loop: The primary AI responds—either accepting suggestions or justifying its implementation choices.
9. Continuous Learning: Both AI agents log lessons from each interaction, refining future performance.
10. Outcome: We achieve approximately 98% of code changes ready for deployment prior to human review.

One of the most fascinating aspects is observing AI agents engage in concept debates and code justifications within GitHub comments. This process effectively teaches them to improve collaboratively—much like seasoned developers learning from peer review discussions.

To illustrate this process, we’ve put together a concise 10-minute demonstration outlining our entire system: Watch the walkthrough here.

While our current focus is on development automation, we’re exploring broader applications of this approach—such as enhancing customer support workflows. We’d love to hear from others experimenting with AI-driven systems, especially in areas like marketing or operations.

Indeed, it’s an exhilarating era for innovation and rapid development. We’re excited to see where these advancements will lead.