Building an Open Source Developer Experience Agents will Love

As AI coding agents increasingly use open-source libraries and contribute issues and pull requests, DBOS argues that libraries must be designed and documented for both agents and human developers. DBOS improved its documentation with generated llms.txt files and raw Markdown pages, but found that static documentation may be outdated in model training data or insufficient for less common features. Large documentation prompts gave agents current API information but became unwieldy, consuming context and reducing performance. Agent skills, which load a small index first and retrieve detailed references only when needed, proved more effective and maintainable, though agents can still struggle to identify the appropriate skill from vague user requests. For production debugging, DBOS built an open-source Model Context Protocol server that lets agents directly query workflow observability and management data, allowing them to inspect failures, analyze running systems, and suggest fixes without manually copying logs into prompts.

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