Building Bitrise's AI platform: Scaling AI features across teams

In the final installment of Bitrise's series on integrating AI, the company discusses the creation of an internal AI Platform to unify its AI initiatives across the organization. As multiple teams independently developed AI-powered features, Bitrise identified the need for a cohesive system to share functionalities, libraries, and best practices, leading to the development of a platform with common building blocks. These include observability for agent statistics, a benchmarking framework, and a monetization strategy. The platform heavily relies on large language models (LLMs) from providers like Anthropic, with Bitrise implementing its own LLM proxy to manage interactions and costs. The AI platform supports two types of agents: sandboxed agents for comprehensive code interactions and central agents for instant responses with limited data access. The platform also emphasizes robust testing and benchmarking to ensure performance and reliability, allowing Bitrise teams to focus on customer-centric solutions rather than infrastructure challenges. Through this series, Bitrise aims to offer insights for others developing AI features.

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