How We Used Our Own Agentic AI to Boost Development Velocity

Gitar reports that internally using its own agentic AI software has increased pull-request merge throughput fivefold, reducing the time required for each 1,000 merged PRs from 80 days before agents to 15 days after a full rollout, while maintaining team size and PR complexity. The company says its AI agent now initiates much of the code output and has recently committed more code than any individual engineer, allowing developers to focus less on repetitive implementation and more on directing, reviewing, testing, and correcting multiple parallel changes. Dogfooding also serves as a product-development feedback loop, helping engineers identify usability gaps, improve agent reasoning and tools, and create reusable prompts and workflows. This transition has changed collaboration by expanding reviews to include agent plans, reasoning, and diffs, while motivating new tooling for overseeing concurrent AI-led work, resolving cross-branch conflicts, and managing coding goals through structured natural-language interfaces.

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