Our Journey to Boosting Development Speed by Tenfold Using Agentic AI Coding and a Tailored “Orchestration” Layer
Transforming Software Development Efficiency with AI-Powered Orchestration: A Deep Dive into Our Workflow Innovation
At our organization, we’ve been pushing the boundaries of software development by integrating cutting-edge AI tools into our daily operations. Recently, we’ve achieved a remarkable tenfold increase in development speed by leveraging AI-driven coding assistants combined with a custom “Orchestration” layer—an innovative approach that orchestrates AI collaboration and automates key steps in our workflow.
The Core Innovation: AI as Collaborative Developers
Unlike traditional approaches where AI tools simply generate code, our system employs multiple AI agents that not only write but also review each other’s work in real time. This collaborative process ensures higher accuracy, efficiency, and code quality while drastically reducing manual review cycles.
Our Workflow Unveiled
Here’s an outline of how our AI-integrated process functions:
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Task Initiation: A project manager assigns a new task within our project management system.
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Task Processing: A dedicated AI agent retrieves upcoming tasks via custom commands tailored to our workflow.
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Contextual Analysis: The AI thoroughly examines our existing codebase, including design documents and relevant web research, to inform its work.
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Detailed Specification: It then creates comprehensive task descriptions, including specific test coverage and quality benchmarks.
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Implementation: The AI develops production-ready code aligned with our coding standards and best practices.
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Automated Pull Requests: Once the code is generated, an AI automatically opens a pull request on GitHub.
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Immediate Peer Review: A secondary AI agent reviews the changes line-by-line, providing constructive feedback.
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Iterative Refinement: The original AI responds to the review, either accepting suggestions or defending its implementation, fostering a mini-debate that mirrors peer review.
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Continuous Learning: Both AI agents learn from these interactions, refining their understanding for future tasks.
The Results Speak for Themselves
Before human intervention, the AI systems produce code that is approximately 98% production-ready. This means our developers spend less time reviewing routine implementation details and more time focusing on higher-level architecture and innovation.
Watching AI Developers in Action
An exciting aspect of this approach is observing the AI agents “debate” implementation choices directly within GitHub comments. It’s almost like watching digital apprentices teach each other, gradually improving their skills and understanding of our codebase.
Learn More
We’ve documented this process in a short 10-minute walkthrough video, providing an inside look at how the orchestration works in practice
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