Software Development (SD) is remarkably dynamic and is critically dependent on the knowledge acquired by the project's software developers as the project progresses. Software developers need to understand large amounts of information related to the tasks at hand. This information (context) is often not explicit, as it can be lost in large documentation repositories, a team member's brain, or beyond their cognitive memory capacity. These contexts include tool features, integration strategies, data structures, code syntax, approaches to tasks, project definitions, and even implicit or tacit contexts, which add significant complexity to the SD process. Current software development practices still lack sufficient techniques using the existing SD execution information and context to provide developers with relevant process guidance, augmenting their capacity to do their job using available applicable information. This paper presents ongoing and future research on an approach to support conversational agent-based knowledge-augmented software development. Developers benefit by receiving recommendations about task-related information and workflows they need to execute. This work advances human-computer interaction patterns in workflow engines, from graphical user interfaces to conversational patterns in software engineering.