Block Introduces BuilderBot, an AI agent orchestration layer
Scale. It’s the issue that causes so many AI initiatives to fail. AI code assistants work remarkably well in individual code repositories, but fail when pushed to handle massive volumes of code and services.
Block, creators of the goose AI agent framework and co-developers of the Model Context Protocol with Anthropic, has released Builderbot, an orchestration layer that manages multiple agents across the Block codebase. It runs in Slack, and users provide a description of what they need, and Builderbot works in the thread to address the issue, be it a bug fix or a new feature. By using Slack, team members can collaborate in real team, watching the bot “research, plan and implement while they steer the direction … the conversation is the development environment,” the company wrote in a blog announcement. Humans, it said, step in to add value when needed.
Builderbot understands the full context of the codebase but operates only on source code and system configuration; it does not touch customer data, PII, or payment information.
“The best way to think about Builderbot is as the missing layer between AI coding tools and how engineering actually works at scale,” said Brad Axen, Head of AI Capabilities at Block. “It handles the orchestration, the context, the environment, so our engineers can focus on the problems worth solving. On the Square side, we took a list of features sellers had been waiting on for months and our engineers shipped them in days. Builderbot handled the scaffolding and the repetitive work, and our engineers made the decisions that shaped the product. It means an idea can go from backlog to live in front of millions of customers in days instead of months.”
Open foundations
Builderbot is built on goose, an open source agent framework that was the first contribution to the Agentic AI Foundation (AAIF) under the Linux Foundation. Over the last quarter, Block AI has contributed to projects including TensorFlow, Hugging Face, and released several key datasets under permissive licenses.
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