Enterprise software development relies on diverse tools and knowledge sources, such as issue trackers (e.g., Jira), version control systems (e.g., GitHub, Bitbucket), and documentation platforms (e.g., Confluence). Developers often encounter context fragmentation, cognitive overload, and operational inefficiencies due to navigating these disparate systems. While retrieval-augmented generation (RAG) has advanced document-based question answering, most existing solutions fail to integrate live operational tools or orchestrate workflows across multiple sources. We introduce AgentHub, an open-source AI agent framework that seamlessly combines semantic knowledge retrieval with tool orchestration. This enables a unified conversational interface for querying, correlating, and acting upon enterprise data. AgentHub continuously synchronizes knowledge sources into a vector database, integrates live APIs from tools like Jira, GitHub, and Confluence, and supports secure action execution (e.g., merging approved pull requests). The framework's document ingestion process is versatile, supporting a wide range of sources including Confluence, web URLs, S3, Google Drive, Azure Blob Storage, and local file systems, with provisions for end-to-end encryption and exclusion of sensitive files. In this paper, we detail the system architecture, implementation, and insights from early deployments, highlighting AgentHub’s ability to minimize context switching, enhance workflow efficiency, preserve institutional knowledge, and facilitate AI-driven enterprise operations.
Odofin et al. (Sat,) studied this question.